1. IMPACT EVALUATION PBAF 526 Class 5, October 31, 2011

2. Today Reflections on Assignment 2? Continue thinking about research design Impact Evaluation How certain can we be? Do we have to be? Block Watch Random Assignment, Outcomes, and Indicators Issues in Impact and Random Assignment: Youth Transition Demonstration Who is randomized? Sample size, power, and effect size Who’s in the average?

3. Block Watch Random Assignment, Outcomes, and Indicators What random assignment protocol would you use to assess the impacts of Block Watch? What are the strengths and weaknesses of your approach? What are the key outcomes you want to assess? What are indicators for those?

4. Youth Transition Demonstration Evaluation Plan Background on YTD evaluation plan The basics of Impact size and significance Power and sample size No Shows/ Intent to Treat vs. Treatment on the Treated Multiple Comparisons Regression adjusted comparisons

5. Youth Transition Demonstration Targets youth receiving disability payments to help in transition to adult life and employment Goals: increase earnings, decrease costs, facilitate transition to self-sufficiency Six program sites with variation in programs Services Waiver of benefit decrease with earnings Education, job training, work placements Case management, counseling, referral to services

8. YTD Evaluation: Selected 6 sites for demonstration and evaluation Intervention built on research from past programs and evaluations Randomly assigned youth to treatment or control Large sample sizes to allow identification of smaller effects and sub-group effects Process and Impact Evaluation Data collected from administrative files, surveys before and after program Advisory group of experts

10. Sampling Why did they divide the list of potential participants (sampling frame) into groups of 10 for contact? Why did they randomize 55 percent to the treatment? Why get pre-intervention characteristics if they are randomly assigning groups?

11. Comparisons may be: -over time -across intervention groups with and without program; levels of intervention (“dosage”) Impact here!

12. Statistical significance When can we rule out having an impact IF there is no impact? Compare 2 means from independent samples: Means: Proportions: Pooled sample variance:

13. Compare 2 means from independent samples: Means: Proportions: Pooled sample variance:

14. Compare 2 means from independent samples: Means: Proportions: Pooled sample variance:

15. Compare 2 means from independent samples: Means: Proportions: Pooled sample variance:

16. So, it’s easier to say impact is “real” (not just randomness) if: Size of impact is larger Variation in outcomes is small (S) Sample sizes are larger Same factors figure into deciding how big a sample we need to find the effect if it’s there! [Power, sample size, minimally detectable effects]

17. Power and sample size: Given randomness, what % of time will you be able to rule out the null, IF it is NOT true (there IS an impact)? How big a sample size do you need to rule out NO effect if the program DOES have an impact? (Rossi et al p.312)

18. Online Calculators for Sample size and Power: Sample size: http://www.dssresearch.com/toolkit/sscalc/size_a2.asp http://www.dssresearch.com/toolkit/sscalc/size_p2.asp Power: http://www.dssresearch.com/toolkit/spcalc/power_a2.asp http://statpages.org/proppowr.html Lots of other sites: http://statpages.org/index.html#Power To calculate sample size and power, you need to estimate both effect of the program and the amount of statistical noise.

19. Minimum Detectable Impacts What are the smallest effects you will be able to detect given n and predicted S?

20. Adjustments to impact assessment: Regression adjusted impacts decrease S and increase power by controlling for “noise” using baseline characteristics Multiple Comparisons are a problem because randomness happens if you look long enough! MDRC picked “primary outcomes” Use adjustments to account for multiple comparisons

21. Showing estimated impacts over time in program

22. Who’s in the average? “No shows” in treatment group didn’t get any services Unlikely to be similar to “shows” If drop, then may overstate potential impacts “Intent to Treat” outcomes include outcomes for no-shows “Treatment on the Treated” outcomes do not include no-shows Non-response to follow-up surveys could bias impact assessments Use administrative data available for all for key outcomes Put resources into follow up to minimize non-response Construct weights to make survey sample estimates comparable to baseline sample

23. Lessons from Summary: Randomization is hard Need to use power analysis to choose target sample sizes Even randomization may not give comparable baseline characteristics Regression may increase comparability and precision Worry about who we have outcome information for (both control and treatment)

24. EXTRA SLIDES

25. A Note About Sample Size When you want to calculate the sample size needed to estimate the differences between two groups, we usually want equal sample sizes. We use the same equation that one would for making an estimate for one sample, but use a measure of the variance that combines information for both populations. For sample size for estimating the difference between population means For sample size for estimating the difference between 2 population proportions For small populations, use the finite population correction (without replacement) This is the with replacement n. Where N is the size of the population, n is the with replacement sample size, and n wor.

26. Practical Significance of Statistical Significance Difference on the original measurement scale Comparison with test norms of performance of a normative population Differences between criterion groups Proportion over a diagnostic or other success threshold Proportion over an arbitrary success theshold Comparison with the effects of similar programs Conventional guidelines Rossi p. 318-319

27. Adjustments for Multiple Testing Solution by Bonferroni: If k=number of comparisons, then α b = α/k. Very conservative. Solution by Benjamini-Hochberg (BH): Adjusts for false discovery rate. Rank p values from smallest to largest Largest p value remains as it is Second largest value is multiplied by the number of comparisons left in the list divided by its rank. If less than.05, then significant. And so on. Other solutions, too!