Research Resources

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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).

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  • Guide to Publishing Research Data

    J-PAL promotes the publication of de-identified data from randomized evaluations. This resource provides guidance on doing so in the form of a checklist for preparing data for submission. It also includes sample informed consent language and other considerations during project planning and before publication, and provides a description of trusted digital repositories that can host data. This...

  • Guide to De-Identifying Data

    This guide provides further details on the de-identification process, including various procedures for de-identifying a dataset, a list of common identifiers that need to be reviewed, and sample code that can be used to de-identify data intended for publication. It is intended to be used alongside the accompanying Guide to Publishing Research Data.

  • 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.

  • Guide: Writing Randomization Code in Stata

    This step-by-step guide uses data and an annotated Stata do-file to illustrate how a simple randomization can be carried out using Stata.

  • 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.

Guide to De-Identifying Data

Researchers who plan to publish data on human subjects should take careful steps to protect the confidentiality of study participants through data de-identification—a process that reduces the risk of re-identifying individuals within a given dataset. This guide provides further details on the de-identification process, including various procedures for de-identifying a dataset, a list of common identifiers that need to be reviewed, and sample code that can be used to de-identify data intended for publication. It is intended to be used alongside the accompanying Guide to Publishing Research Data.

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Guide to Publishing Research Data

J-PAL promotes the publication of de-identified data from randomized evaluations. This resource provides guidance on doing so in the form of a checklist for preparing data for submission. It also includes sample informed consent language and other considerations during project planning and before publication, and provides a description of trusted digital repositories that can host data. This guide is intended to be read alongside the accompanying Guide to De-Identifying Data.

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Slides: Generalizability Framework

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A Practical Guide to Measuring Women and Girls’ Empowerment in Impact Evaluations

This guide offers practical tips for how to measure women and girls’ empowerment in impact evaluations.

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A Practical Guide to Measuring Women and Girls’ Empowerment in Impact Evaluations: Appendix 1

Appendix 1 of A Practical Guide to Measuring Women and Girls’ Empowerment in Impact Evaluations provides a catalogue of examples of survey questions and modules related to women and girls’ empowerment that have been used by J-PAL affiliated researchers in previous impact evaluations.

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A Practical Guide to Measuring Women and Girls’ Empowerment in Impact Evaluations: Appendix 2

Appendix 2 of A Practical Guide to Measuring Women and Girls’ Empowerment in Impact Evaluations includes examples of non-survey instruments that can be used to measure women’s empowerment, pros and cons for each approach, and tips on how to use them.

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Six Rules of Thumb for Determining Sample Size and Statistical Power

A guide for policymakers and practitioners that outlines the main factors that affect statistical power and sample size, and demonstrates how to design a high-powered randomized evaluation.

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