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Randomized trials

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Frame of Image andomized trials
o Not magic
o Still subject to basic constraints of statistics
o Need large samples o Drop out, non-compliance a problem o Though not biased, estimated parameters might differ from desired parameters
o Sometimes not politically feasible
Outline
1.
2.
Randomization solves selection bias
i. ii. iii.
What should be the unit of randomization?
Bias Statistical power Externalities
3. 4. 5. 6.
How do you actually randomize? Stratification (what is it, why do we need it) Difference between random sampling and randomization
i. ii. iii.
Other issues
Attrition Compliance (both for subjects and implementers) Estimated parameters
7.
Non-randomized methods
Randomized trials overcome potential confounders
o Let’s return to earlier examples:
o Health insurance o Conditional cash transfers
o Bias 1: Selection bias
o Par ticipants might be innately different from nonpar ticipants Take all eligible people in population of interest Place all names on slips of paper in a jar Pick half of the slips of paper out of jar Chosen names get intervention, those not chosen do not
o Consider a simple lottery
o o o o
Bias 1: Selection bias
Eligible population
o Green = treatment (with intervention) o Pink = comparison (without intervention)
o Assume this array represents geographical spread of sample population
Bias 1: Selection bias
Eligible population
o Green = treatment (with intervention) o Pink = comparison (without intervention) o Should average characteristics differ across treatment


Full Text
Title Randomized trials
Similar Titles
Material Type Proceedings
Author(English)

Beath, Andrew; Trias, Julieta

Publisher

[Washington, DC]:The World Bank

Date 2012
Event

The Regional Impact Evaluation Workshop

Series Title; No Technical Track Session
Pages 48
Language English
File Type Documents
Original Format pdf
Subject Official Aid < Others
Holding KDI; KDI School

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