Data Analysis for Impact Evaluations – Quiz 4

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Question 1 of 5

1. Question

1. Which of the following statements is true about analyzing panel data? (select all that apply)

To run a first-differences model, data must be in long form
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To run a fixed effects model, data must be in long form
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To run an ANCOVA model, data must be in long form
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A first-differences model is biased if you’ve done an RCT
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A fixed effects model is biased if you’ve done an RCT
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An ANCOVA model is biased if you’ve done an RCT
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None of the above
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Correct

Incorrect
Question 2 of 5

2. Question

Reshape the data long so that there are two observations per student.

2. How many test score variables are in the reshaped data?

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Question 3 of 5

3. Question

Run a fixed effects model to estimate the impact of treatment on the change in Hindi test scores. Don’t forget to cluster standard errors by village.

3. What is the estimated treatment effect? Round to the nearest 0.01

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Question 4 of 5

4. Question

4. What is the correct interpretation of the coefficient on the binary variable denoting the testing round?

The average score at endline for the whole sample
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The average score at endline in the treatment group
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The average score at endline in the control group
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The average change in scores from baseline to endline for the whole sample
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The average change in scores from baseline to endline for the treatment group
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The average change in scores from baseline to endline for the control group
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None of the above
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Question 5 of 5

5. Question

Now reshape the data wide and run the equivalent ANCOVA model to estimate the treatment effect on Hindi test scores.

5. Which of the following statements is true about the differences between the ANCOVA model vs the fixed effects model? (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 FE model than in the ANCOVA model
Correct Incorrect Correct answer

The estimated treatment effect is larger in the ANCOVA model than in the FE model
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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 ANCOVA model than in the FE model
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The estimated standard error on the treatment coefficient is larger in the FE model than in the ANCOVA model
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None of the above
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Data Analysis for Impact Evaluations – Quiz 4

Quiz Summary

Information

Results

Results

Categories

1. Question

2. Question

Reshape the data long so that there are two observations per student.

3. Question

Run a fixed effects model to estimate the impact of treatment on the change in Hindi test scores. Don’t forget to cluster standard errors by village.

4. Question

5. Question

Now reshape the data wide and run the equivalent ANCOVA model to estimate the treatment effect on Hindi test scores.

How AI evaluation works in practice: Insights from implementers

Solving the scaling challenge: CARE’s evidence-based approach to sustainable impact

Partnership with the Government of Uttar Pradesh on economic growth

Where AI interventions succeed or fail

IDinsight welcomes Charlene Migwe as East and Southern Africa Lead

The middle-income moment: Navigating evidence-based development in Southeast Asia

Navigating the next frontier: Doing more with less in West and North Africa

Bringing implementation science to AI: The case for process evaluations in the AI evaluation framework

Adapting for impact: Reflecting on a year of uncertainty and reinvention

Lessons from IDinsight’s Generative AI transformation

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Quiz Summary

Information

Results

Results

Categories

1. Question

2. Question

Reshape the data long so that there are two observations per student.

3. Question

Run a fixed effects model to estimate the impact of treatment on the change in Hindi test scores. Don’t forget to cluster standard errors by village.

4. Question

5. Question

Now reshape the data wide and run the equivalent ANCOVA model to estimate the treatment effect on Hindi test scores.

How AI evaluation works in practice: Insights from implementers

Solving the scaling challenge: CARE’s evidence-based approach to sustainable impact

Partnership with the Government of Uttar Pradesh on economic growth

Where AI interventions succeed or fail

IDinsight welcomes Charlene Migwe as East and Southern Africa Lead

The middle-income moment: Navigating evidence-based development in Southeast Asia

Navigating the next frontier: Doing more with less in West and North Africa

Bringing implementation science to AI: The case for process evaluations in the AI evaluation framework

Adapting for impact: Reflecting on a year of uncertainty and reinvention

Lessons from IDinsight’s Generative AI transformation

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