Research Resources

PDF version

Research Resources

Our library of practical resources is available for researchers undertaking randomized evaluations and those teaching the technique to others. The resources presented here are curated by J-PAL in partnership with Innovations for Poverty Action (IPA).

Using Administrative Data for Randomized Evaluations

A guide that provides practical guidance on how to obtain and use nonpublic administrative data for a randomized evaluation.

Slides: Sampling and Sample Size

Learn how sample size and other factors affect statistical power in these slides from J-PAL's Executive Education Course.

IPA's Best Practices for Data and Code Management.

IPA's data publication guideline covers the principles of organizing and documenting data and code – illustrated using examples from Stata – at all steps of the project lifecycle with the goal of making research reproducible.

Slides: What is Evaluation?

Learn why evaluate social programs, what is evaluation, and the components of evaluation in these slides from J-PAL's Executive Education Course.

Slides: Why randomize?

Learn about why, when properly designed and conducted, randomized evaluations provide the most credible method of estimating program impact in these slides from J-PAL's Executive Education Course.

Resources on How to Obtain and Use Nonpublic Administrative Data

J-PAL North America developed this pair of resources to support the use of nonpublic administrative data for randomized evaluations. The guide provides general tips on how to obtain and use these data. The catalog of key US data sets provides agency-specific information on how to request data.

Stata 102

In this Stata module, you have some Stata experience (say in a college class) but would not consider yourself particularly comfortable with the program. You are very familiar with the following concepts:

  • Descriptive commands such as summarize, tabulate, and list
  • Conditions: if, and (&), and or (|) 
  • Data manipulation commands such as generate, replace, and drop

You are likely somewhat familiar with:

  • Creating and writing do-files
  • Sorting and saving datasets