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Building Evidence in Education: Workshop for EEF evaluators 2 nd June: York 6 th June: London

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The EEF by numbers 83 evaluations funded to date 3,000 schools participating in projects 34 topics in the Toolkit 16 independent evaluation teams 600,000 pupils involved in EEF projects 14 members of EEF team £220 m estimated spend over lifetime of the EEF 6,000 heads presented to since launch 10 reports published

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Session 1: Design RCT design, power calculations and randomisation Ben Styles (NFER) Maximising power using the NPD John Jerrim (Institute of Education)

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RCT design Power calculations and randomisation Ben Styles Education Endowment Foundation June 2014

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RCT design The ideal trial Methods of randomisation Power calculations Syntax exercise!

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A statistician’s ideal trial Randomly select eligible pupils from NPD No consent! Simple randomisation of pupils to intervention and control groups No attrition No data matching problems No measurement error

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BEFORE YOU START ! 1. Trial registration: specification of primary and secondary outcomes in addition to sub- group analyses 2. Recruit participants and explain method to stakeholders 3. Select participants according to fixed eligibility criteria 4. Obtain consent 5. Baseline outcome measurement (or use existing administrative data) 6. Randomise eligible participants into groups (evaluator carries out randomisation) 7. Intervention runs in experimental group; control receives ‘business-as-usual’/an alternative activity 8. Administer follow-up measurement (evaluator) 9. Intention-to-treat analysis followed by reporting as per CONSORT guidelines 10. Control receives intervention (under what circumstances?)

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Why we depart from the ideal Schools manage pupils! Nature of the intervention Contamination – how serious is the risk?

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Restricted randomisation? Use simple randomisation where you can Timetable considerations in a pupil-randomised trial → stratify by school Important predictor variable with small and important category → stratify by predictor Fewer than 20 schools → minimise Multiple recruitment tranches → blocked Pairing → BAD IDEA!

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Restricted randomisation Simple randomisationRestricted randomisation Restricted randomisation more complicated and can go wrong. Take strata into account in analysis:

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To remember! If you have restricted your randomisation using a factor that is associated with the outcome (e.g. school) THEN INCLUDE THE FACTOR AS A COVARIATE IN YOUR ANALYSIS

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Chance imbalance at baseline As distinct from bias induced by measurement attrition Can be quite large in small trials e.g. on baseline measure Include covariate in final analysis

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Sample size calculations School or pupil-randomised? Intra-cluster correlation Correlation between covariate and outcome Expected effect size p(type I error)=0.05; power=0.8 Attrition

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Rule of thumb Lehr, 1992

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Pupil randomised ICC = 0 Correlation between baseline and outcome: uploads/pdf/Pre-testing_paper.pdf and your previous work uploads/pdf/Pre-testing_paper.pdf Effect size: previous evidence; cost- effectiveness; EEF security ratings Attrition: EEF allow recruitment to be 15% above sample size after attrition

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Cluster-randomised Same as for pupils aside from ICC Proportion of total variance that is due to between cluster variance EEF pre-testing paper has some useful guidance Pre-test also reduces ICC e.g. from 0.2 to 0.15 for KS2 baseline, GCSE outcome

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MDES Minimum detectable effect size EEF require this on the basis of real parameters for the security rating (avoid retrospective power calculation) How good were my estimates?

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Sample size spreadsheet (fill in the highlighted boxes)Scenario 1 Expected number of pupils per school being sampled180 ROH (Intra-class correlation - percentage of variance in outcome being studied attributable to school attended)0.15 Deff (adjustment for nested design)27.85 Confidence level (of test we will use to assess effect)95.0% Critical T-value1.96 Correlation between before and after scores0.70 SD of residuals in scores (if scores have SD of 1)0.71 Expected effect size (in terms of absolute outcome scores)0.2 Expected effect size (in terms of residual outcome scores)0.28 n(schools) in intervention31 n(schools) in control31 n(pupils) in intervention5580 n(pupils) in control5580 Expected SE of difference between groups (in SDs)0.10 Power80.0%

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Running the randomisation SYNTAX EXERCISE In pairs, explain what each of the steps does How many schools were randomised in this block?

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Conclusions Always think of any RCT (any quantitative impact evaluation) as a departure from the ideal trial The design, power calculations, method of randomisation and analysis all interrelate and need to be consistent

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Maximising power using the NPD John Jerrim (Institute of Education)

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Structure How much power do EEF trials currently have? PISA, power, star ratings and current EEF trials Exercise Work in groups to design an EEF trial Goal = Maximise power at minimal cost My answers How might I try to maximise power? Your answers! / Discussion

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Power in context Effect sizes, PISA rankings and EEF padlock ratings

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How powerful are EEF trials thus far? EEF secondary school trials As of 01 / 05 / 2014 Detectable effect size Mean = Median = 0.25 Between 4* and 5* by EEF guidelines….

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Power and the PISA reading rankings UK’s current position Effect size = 0.10 Effect size = 0.20 (EEF 5*) Effect size = 0.30(EEF 4*) MEDIAN EEF TRIAL = 0.25 Effect size = 0.40(EEF 3*) IMPLICATION Effect sizes of 0.20 are damn big … particularly given pretty small doses we are giving Effect size = 0.50(EEF 2*)

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Do we currently have a power problem? - Quite possibly! - So trying to get more power in future trials very important…..

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Exercise

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Task: In groups, discuss how you would design the following trial Intervention = Teaching children how to play chess Maximum number of treatment schools = 20 secondary schools Year group = Year 7 Level of randomisation = School level Test = One-to-one non-verbal IQ assessment with trained educationalist (end of year 7) Control condition = ‘Business as usual’ Study type = ‘Efficacy’ study (proof of concept) Objective: Maximise power at minimum cost How would you design this trial to meet these twin objectives? What could you do to increase power in this trial E.g. Would you use a baseline test? If so, what? Exercise

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My answers The usual suspects….. …and less obvious options

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The usual suspects….. 1.Use a regression model and include baseline covariates….. - Adding controls explains variance. Boosts power 2.Use Key stage 2 test scores as “pre-test”…. - Point of baseline covariates is to explain variance - KS 2 scores in maths likely to be reasonably correlated with outcome (non-verbal IQ) - CHEAP! From NPD. 3. Stratify the sample prior to randomisation - Potentially reduces error variance. Thus boosts power. - Additional advantages. Balance of baseline characteristics. 4. Really engage with control schools - Make sure we minimise loss of sample through attrition

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Less ‘obvious’ options….

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Don’t test every child…….. There are around 200 children per secondary school….. …. One-to-one testing is expensive …Testing more than 50 pupils buys you little additional power RANDOMLY SAMPLE PUPILS WITHIN SCHOOLS! Assumptions 20 schools Pre/post corr of % power Rho = 0.15

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…..use an unequal sampling fraction We all know that ↑ clusters (k) means ↑ power This example: limited to only a small number of treatment schools (20) ….but control condition was non-intrusive and cheap So don’t just recruit 20 control schools as well – recruit more! Nothing about RCT’s mean we need equal k for treatment and control Power calculation becomes more complex (anybody know it!?)

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Use more homogenous selection of schools…. ALL UK SCHOO LS LOW PERFORMING SCHOOLS ONLY

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Why does rho decline?? The within school variation barely changes ….. …. While the between school variation declines substantially

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Implications As example is an efficacy study why not restrict attention to low performing schools only? - Boosts power! - Fits with EEF mandate (close performance gap) - Not worried about generalisability We implicitly do this anyway (e.g. by doing trials in just one or two LA’s)…… …..but can we do it in a smarter way??? Little appreciated trade-off between POWER and GENERALISBILITY - Long-term implications for EEF - Trial representative of England population very hard to achieve

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Conclusions Do we have a “power problem”? Quite possibly Median detectable effect size = 0.25 in EEF secondary school trials If were to boost UK reading PISA scores by this amount, we would move above Canada, Taiwan and Finland in the rankings….. Ways to potentially increase power Include baseline covariates (from NPD where possible) Stratify the sample prior to randomisation Engage with control schools! Do you need to test every child? Practical alternatives? Could you increase number of control schools without adding much to cost (unequal randomisation fraction) Could you restrict your focus to a narrower population? (e.g. low performing schools only)?

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