Our library of practical resources is intended for researchers and research staff undertaking randomized evaluations, as well as those teaching the technique to others, and anyone interested in how randomized evaluations are conducted.
Incorporating lessons learned through our own experience and through guidance from researchers and research organizations, we provide practical advice for designing, implementing, and communicating about evaluations. These resources are a collaborative effort. We credit the authors of all the resources we post here, and link to their original work wherever possible.
Please reach out to us at [email protected] or fill out this form with questions or feedback.
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Introduction to Randomized Evaluations
Resources
A non-technical overview and step-by-step introduction for those who are new to randomized evaluations, as well as case studies and other teaching resources.
Before Starting a Project
Resources
Tips on successful field management and implementation partnerships for researchers who are new to fieldwork.
Project Planning
Resources
Highlights include: annotated checklist for designing an informed consent process, detailed advice on grant proposals and budgeting, and suggestions for proactive measures to help ensure ethical principles are followed in research design and implementation.
Research Design
Resources
Data Collection
Resources
This section contains guidance specific to working with surveyors or survey companies, information about administrative data collection, and information applicable to all modes of data collection, such as on data security, data quality, and grant management.
Processing and Analysis
Resources
All the steps in a research project after the data was collected or assembled, from data cleaning to communicating results.
Survey programming
This resource covers best practices for programming a survey using computer assisted personal interview (CAPI) software. We primarily rely on examples using SurveyCTO, which is widely used by J-PAL...
Data quality checks
High-frequency checks, back-checks, and spot-checks can be used to detect programming errors, surveyor errors, data fabrication, poorly understood questions, and other issues. The results of these...
Data cleaning and management
Although quality control checks in the field should catch most errors in data collection, there are a number of steps you will have to undertake to prepare your collected data for analysis. This...
Data visualization
Data visualization can be helpful at many stages of the research process, from data reporting to analysis and publication. Relative to regression tables, cross-tabs, and summary statistics, data...
Data analysis
This guide provides an overview of data analysis for randomized evaluations in order to estimate causal impact. It is intended to provide something of a starting point and orient individuals not...
Data de-identification
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...
Data publication
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...
Conducting cost-effectiveness analysis (CEA)
This resource is intended for researchers who are interested in collecting cost data and conducting comparative cost-effectiveness analysis (CEA) for their evaluation. It provides an overview of CEA...