Data Analysis for Impact Evaluations – Quiz 5

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Question 1 of 4

1. Question

1. Which of the following are NOT reasons for stratifying your treatment assignment? (select all that apply)

Stratifying reduces the risk of spillovers
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Stratifying ensures that you have a large enough sample for estimating treatment effects in subgroups
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Stratifying improves the precision of impact estimates
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Stratifying reduces the risk of crossovers
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Stratifying reduces the risk of bias when estimating treatment effects
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All of the above are reasons for stratifying
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Question 2 of 4

2. Question

2. If you run the command “areg…, absorb(strataID)”, what is this equivalent to doing in your regression?

Clustering standard errors at the strata-level
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Estimating heteroskedasticity-robust standard errors
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Including a dummy variable for each strataID in the sample
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Including a dummy variable for each individual in the sample
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Estimating interaction effects for each strataID
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Nothing – this is equivalent to running the normal ‘regress’ command
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Question 3 of 4

3. Question

Regress Hindi Year 3 scores on treatment and Hindi baseline scores, and cluster at the village-level. Now run this regression again and include village pair fixed effects.

3. Which of the following statements is true about the differences between the first regression and the second regression? (select all that apply)

The estimated treatment effects are the same in the two models
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The estimated treatment effect is larger in the model with strata fixed effects than in the model without strata fixed effects
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The estimated treatment effect is larger in the model without strata fixed effects than in the model with strata fixed effects
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The estimated standard errors on the treatment coefficients are the same in the two models
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The estimated standard error on the treatment coefficient is larger in the model with strata fixed effects than in the model without strata fixed effects
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The estimated standard error on the treatment coefficient is larger in the model without strata fixed effects than in the model with strata fixed effects
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None of the above
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Question 4 of 4

4. Question

4. What is the correct interpretation of the coefficient on hindi_bl in these regressions?

The average effect of treatment on Hindi endline scores
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The average effect of treatment on Hindi baseline scores
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The average change in Hindi baseline scores per 1 unit increase in Hindi endline scores
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The average change in Hindi endline scores per 1 unit increase in Hindi baseline scores
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The average Hindi baseline score in the control group
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The average Hindi endline score in the control group
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None of the above
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Data Analysis for Impact Evaluations – Quiz 5

Quiz Summary

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Results

Categories

1. Question

2. Question

3. Question

Regress Hindi Year 3 scores on treatment and Hindi baseline scores, and cluster at the village-level. Now run this regression again and include village pair fixed effects.

4. Question

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Quiz Summary

Information

Results

Results

Categories

1. Question

2. Question

3. Question

Regress Hindi Year 3 scores on treatment and Hindi baseline scores, and cluster at the village-level. Now run this regression again and include village pair fixed effects.

4. Question

Understanding the gig economy: Lessons from drivers in India, Indonesia, and Kenya

Data infrastructure and institutional readiness as the foundation for AI impact

Doing more with less: What a room full of global development leaders said about cost-effectiveness

Focusing Dignity

How to prepare your organization for impactful AI adoption

Observations from CIES 2026: What the education field is actually wrestling with

Turning cost data into a tool for impact

Ronald Abraham succeeds Neil Buddy Shah as IDinsight Board Chair

IDinsight and the National Assembly of Zambia sign Memorandum of Understanding to strengthen evidence-based governance

Delivering Resilient Enterprises and Market Systems (DREAMS) – Impact Evaluations

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