1 / 24

Randomized Evaluation: Do s and Don’t s

Randomized Evaluation: Do s and Don’t s. An example from Peru Tania Alfonso Training Director, IPA. Outline. Design Implementation Analysis. Outline. Design Research question Power Randomization Sampling Implementation Analysis. Research question.

martena-burnett
Download Presentation

Randomized Evaluation: Do s and Don’t s

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Randomized Evaluation: Dos and Don’ts An example from Peru Tania Alfonso Training Director, IPA

  2. Outline • Design • Implementation • Analysis

  3. Outline • Design • Research question • Power • Randomization • Sampling • Implementation • Analysis

  4. Research question • Do make sure the research question is policy relevant • Do make sure your indicators are answering your research question

  5. Power • Don’t conduct an under-powered evaluation • What does it mean to be under-powered? • Sample size and power

  6. Power • Do power calculations first • Effect size • Sample size • Getting data • (What will take-up be?)

  7. Power • Do cluster your standard errors when doing power calculations • Bad examples (two districts, 10,000 people)

  8. Randomization • Do Ensure balance • Stratification • Re-randomizing • Costs and benefits

  9. Sampling • Do make sure your sampling frame is as close to your target population as possible • Effect size

  10. Outline • Design • Implementation • Measurement • Monitoring • Attrition • Analysis

  11. Measurement • Don’t collect data differently for treatment and control groups • Introducing bias

  12. Measurement • Don’t use as your primary indicator something that may change with the intervention, even when the outcome does not

  13. Monitoring • Do monitor your intervention to ensure the treatment groups are receiving the treatment, and control groups are not • Contamination

  14. Monitoring • Do collect process indicators to unpack the black box

  15. Attrition • Do whatever it takes to minimize attrition • Attrition bias

  16. Outline • Design • Implementation • Analysis • Treatment integrity • Attrition • Final outcomes • Subgroup analyses • Covariates

  17. Integrity of design“Once in treatment, always in treatment” • Don’t switch treatment or control status, based on compliance • Intention to Treat • Treatment on Treated

  18. Attrition“Once in sample, always in sample” • Do not ignore “attritors” • Attrition bias

  19. Attrition • Don’t relax just because rates of attrition are the same in treatment and control groups • How do we test • How do we know

  20. Final outcomes • Don’t run regressions on 20 different outcomes and only report on 1 or 2 “significant impacts” • Do report on all outcomes

  21. Sub-group analysis • Don’t run regressions on 20 different subgroups and only report on 1 or 2 “significant impacts”

  22. Covariates • Do specify the regression(s) you plan to run beforehand • Do include covariates that you stratified on and those helpful for absorbing variance.

  23. External Validity • Do be modest about the external validity of your results • Consider the context (needs assessment) • Consider the process (process evaluation)

  24. Cost effectiveness • Do have listened to Iqbal’s lecture yesterday • Not sure if he is presenting or covered this…just a guess…

More Related