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The Multi-Site RCT• A fleet of RCTs!• Each conducted in a different social setting• Since 2002, IES has funded 175 randomized
trials;• Vast majority are multi-site trials (Spybrook, 2013)
Opportunities
• Can assess generalizability of impact– Gauge variation in effect– Model heterogeneity– Does the intervention equalize outcomes?
But we must formulate appropriate analyses.This is trickier than it might seem!
Some Recent MS TrialsStudy Levels Assigned
UnitsSites Fixed or
Random sites
National Head Start Eval.
2 Children 300+ Program Sites
Random
Moving to Opportunity
2 Families 5 cities Fixed
BostonCharter School Lotteries
2 Children Lottery pools Random
Tennessee STAR
3 Teachers Schools Random
Double-Dose Algebra
2 Children Schools Random
Theoretical Model
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4. Unbalanced Designs and Targets of Inference
Two kinds of imbalance
• Unequal n per site• Unequal propensity score (probability of
assignment to the treatment group)
Targets of Inference
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Properties
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b)Centering HLM B,Var(B) Bias in β if precision is related to B (less so than fixed effects )
c) Control propensityScore
B,Var(B)?? Similar to Centering for β,
d)Weighting (“IPTW”) with HLM
B,Var(B),Cov(B,U0) Removes bias (but may be imprecise!)
a) Fixed Effects Model
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Summary so far
Four commonly used options* limit what we can estimate * produce inconsistent estimates of
what we can estimate.
6.HLM with Inverse Probability of Treatment Weighting
• IPTW• Embedding within HLM• Always produces consistent estimates• May be quite imprecise
Inverse Probability of Treatment Weights (Robins and Greenland, 2000)
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Weighting Schemes for HLM
Level-2 weight
Level-1 weight Result
Weights site-specific estimates of impact by
Weights site-specific estimates equally
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Estimation via Weighted log Likelihood(weighted complete-data log likelihood)
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Two examples of weighting schemes
Equally weight people Equally weight sitesLevel-1 weight Level-1 weight
Level-2 weight Level-2 weight1
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7. DiscussionA Class of Estimators…
We are approximating various balanced designs by maximizing the weighted log likelihood
This gives us a family of method-of-moments estimators
Hence no reliance on normality or homoscedasticity
May be quite imprecise
Extensions• Extend easily to multi-site clustered
randomized designs• Balance propensity within covariate classes
in randomized studies• Extend to observational studies• Extend to weighted mediation models (Hong
et al, 2012)• Application to local average treatment effects
(Raudenbush, Nomi, and Reardon, 2016)