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Session 4: Trial management Recruitment and retention: the role of evaluators Meg Wiggins ( IoE ). Sub-brand to go here. Recruitment and Retention: the evaluator’s role. Meg Wiggins – Institute of Education, London . Recruiting schools. Retaining schools. Project examples.
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Session 4: Trial managementRecruitment and retention: the role of evaluatorsMeg Wiggins (IoE)
Sub-brand to go here Recruitment and Retention: the evaluator’s role Meg Wiggins – Institute of Education, London
Intervention: 30 hours of primary school classroom chess teaching, delivered by external CSC tutors Cluster trial, randomised at school level Evaluation team at IoE: John Jerrim (Lead), Lindsey Macmillan, John Micklewright Process evaluation - Meg Wiggins, Mary Sawtell, Anne Ingold Example 1
Chess in Schools - Recruitment • Community organisation – small central staff team • Recruitment expectations – return to known ground • Recruitment reality – IoE provided lists of schools selected on FSM % criteria, in their chosen Las • Capacity issues, limited understanding about RCTs, huge enthusiasm for the evaluation
Chess in Schools – Recruitment 2 • Nearly reached target of 100 primary schools within tight timeframe • Succeeded by tenacious, labour intensive direct contact by phone • Often before school; strategies for speaking directly to head teachers • Ditched letter and emails as first approach • Brought in dedicated person to recruit
Chess in Schools – Recruitment 3 • As evaluators we assisted recruitment by: • Providing extra schools from which to recruit • Providing extra time for recruitment • Channeling enthusiasm - providing focus
Chess in Schools – Retention in study • Study designed to limit retention challenges • Influenced by learning from earlier IoE EEF evaluations • No testing within schools; use of NPD data • Collection of UPNs before randomisation
Chess in Schools – Retention in study • Pre-randomisation baseline head teachers’ survey • Showed some confusion about the trial and intervention • Limited evaluation involvement in development of materials used in recruitment of schools • How much were they used? • Lack of forum for cascading study information beyond head/SLT
Chess in Schools – Retention in intervention • Most intervention schools adopted the programme • CSC tell us that nearly all have completed the full 30 week intervention • End of intervention survey pending of tutors & teachers to confirm this Case study work flagged variation in schools re: lessons replaced by intervention • Important to study; not critical for schools/Chess tutors
Chess in Schools – Lessons learnt • Beyond recruitment – importance of forum for cementing the key study messages within schools • Tension between role as impartial evaluator observing from a distance and partner in achieving a successful intervention and evaluation • Plan some interim formal means of assessing implementation and intervention retention • Design of the study means that retention issues remain minimal
Early Language Learning & Literacy (ELLL) Project Example 2 Early Language Learning & Literacy (ELLL) Project • Intervention: Training primary class teachers to deliver a curriculum of French lessons as well as follow up activities linking the learning of French to English literacy. • Cluster trial, randomised within schools at class level, across two year groups (3 & 4) • IoE evaluation team: Meg Wiggins (Lead), John Jerrim, Shirley Lawes, Helen Austerberry, Anne Ingold
Early Language Learning - Recruitment • Design of study influenced by: • Tight study timeline – curriculum changes – required post intervention testing • Extremely short recruitment window prior to commencement of teacher training • Capacity to deliver intervention to limited numbers • Challenges in determining inclusion criteria for schools • Key issues around specialist language teachers and within schools randomisation design • Over burdening of London schools – EEF issue
Early Language Learning - Recruitment • Compromises reached: • Outside organisation brought in to recruit • London schools allowed • Relaxation of ban on specialist teachers (slight!) • Close liaison between CfBT and evaluation team • Case by case basis recruitment • Development of detailed recruitment materials – FAQs • Minimum target of 30 schools exceeded – 46 randomised
Early Language Learning - Retention • Immediate post randomisation drop out: 9 schools • 2 couldn’t attend teacher training dates • 2 schools disagreed with randomisation • 5 never responded to invitation to teacher training • Additionally, 4 schools dropped one year group, but stayed in trial with other year group • Within one week – 46 schools reduced to 37!
Early Language Learning -Retention • Evaluation team attended each training session and explained study to intervention teachers • Found almost no knowledge of study had been cascaded down by heads • Emphasised randomisation and no diffusion • Answered many questions! Learnt from them! • Provided teachers FAQs sheet • Explained plans for end of year testing
Early Language Learning - Retention • Used additional training events to continue evaluation presence • All 37 schools have delivered (most of) the intervention • Organising testing dates (mostly by email) has been fairly straightforward • Lots of messages back and forth to finalise • Testing begins Tuesday
Early Language Learning – Lessons Learnt • Tight recruitment period led to inclusion of schools that weren’t committed. Role of external recruitment agency? • Tension between confusing schools with contacts from programme and evaluation teams vs. not having evaluation messages clearly conveyed. • Need to ensure evaluation messages reach those that deliver interventions, not just to Heads. • Allowing time and resources for communicating with schools at every stage – no shortcuts to personal contact.
Task - table discussion and feedbackWhat one top tip or suggestion would you make for recruitment, retention or communication with schools?
My conclusions • Design with recruitment and retention at the fore • There is no substitution for evaluation team direct contact with schools – allocate resources accordingly • Be flexible – balance rigour with practicality. Choose your battles!
Session 4: Analysis and reportingAnalysis methods and calculating effect sizesBen Styles (NFER)Analysis Plans: A cautionary taleMichael Webb (IFS)
Analysis and effect size Ben Styles Education Endowment Foundation June 2014
Analysis and effect size • How design determines analysis methods • Brief consideration of how to deal with missing data • How to calculate effect size
‘Analyse how you randomise’ • Pupil randomised • The ideal trial: t-test on attainment • Usually have a covariate: regression (ANCOVA) • Stratified randomisation: regression with stratifiers as covariates
‘Analyse how you randomise’ • Cluster randomised (think about an imaginary very small trial to understand why) • t-test on cluster means • Regression of cluster means with baseline means as a covariate • ‘It’s the number of schools that matters’
BUT • If we have an adequate number of schools in the trial, say 40 or more • We have a pupil-level baseline measure • We can use the baseline to explain much of the school-level variance • Multi-level analysis
Missing data • Prevention is better than cure • Attrition is running at about 15% on average in EEF trials • Using ad hoc methods to address the problem can lead to misleading conclusions • http://educationendowmentfoundation.org.uk/uploads/pdf/Randomised_trials_in_education_revised.pdf • Baseline characteristics of analysed groups • Baseline effect size
Effect size • We need a measure that is universal • The difference between intervention group mean and control group mean • As measured in standard deviations
79 Effect size • See EEF analysis guidance at http://educationendowmentfoundation.org.uk/uploads/pdf/Analysis_for_EEF_evaluations_REVISED3.pdf • Write a spreadsheet that does it for you
But what about multi-level models? • Difference in means is still the model coefficient for intervention • But the variance is partitioned – which do we use? • And the magnitude of the variance components change depending on whether we have covariates in the model – with or without?
We want comparability • Always think of any RCT as a departure from the ideal trial • We want to be able to compare cluster trial effect sizes with those of pupil-randomised trials • We want to meta-analyse
Which variance to use • Pupil-level • Before covariates
This is controversial • Before or after covariates means two different things • At York on Monday leaning towards total variance but pupil-level better for meta-analysis • Report all the variances and say what you do
Conclusions • A well designed RCT usually leads to a relatively simple analysis • Some of the missing data methods are the domain of statisticians • Be clear how you calculate your effect size