This resource provides guidance for researchers wishing to register their study in a public trial registry. We list common social science registries and registration policies of common funders and economics journals. We additionally describe registration considerations such as the level of detail to...

A pre-analysis plan (PAP) describes how researchers plan to analyze the data from a randomized evaluation. It is distinct from the concept of pre-registration, which in economics is the act of registering a research project in a registry such as the AEA RCT Registry before the intervention begins...

Randomization for causal inference has a storied history. Controlled randomized experiments were invented by Charles Sanders Peirce and Joseph Jastrow in 1884. Jerzy Neyman introduced stratified sampling in 1934. Ronald A. Fisher expanded on and popularized the idea of randomized experiments and...

This section is intended to provide an intuitive discussion of the rationale behind power calculations, as well as practical tips and sample code for conducting power calculations using either built-in commands or simulation. It assumes some knowledge of statistics and hypothesis testing. Readers...

The goal of measurement is to get reliable data with which to answer research questions and assess theories of change. Inaccurate measurement can lead to unreliable data, from which it is difficult to draw valid conclusions. This section covers key measurement concepts, means of data collection...

This section covers the essentials of survey design. It includes an overview of survey development, practical tips, formatting suggestions, and guides to translation and quality control.

Managing a survey is a massive logistical undertaking. This resource, and the other resources under survey data collection, describes the decisions to make and processes to set up in advance to minimize problems in the field. It includes topics specific to in-person data collection as well as those...

This resource covers practical advice on managing grants for principal investigators (PIs) and research staff. We include overarching tips to help ensure smooth grant management throughout the project; define and explain common terms and conditions; discuss final deadlines and no-cost extensions...

This resource focuses on in-house data collection. Information about outsourcing to survey firms is available in the resource on working with third party firms. Running a large-scale household survey may seem like a daunting task at first, but don’t despair. The trick is to develop a detailed plan...

This section discusses the logistics of managing surveyors and survey teams. Depending on the field team size, you may need to set up processes to manage upwards of 50 field staff. Thinking ahead about how they will be paid, how they should communicate with their supervisors, and protocols for...

This resource provides guidance on when to work with an external survey firm, the process of identifying and contracting a firm, and how to coordinate tasks to ensure a successful study. It highlights topics in survey or study design that may be beneficial to review with the survey firm, outlines a...

Piloting is the testing, refining, and re-testing of survey instruments in the field to make them ready for your full survey. It is a vital step to ensure that you understand how your survey works in the field, that you are collecting accurate, appropriate data. It also helps in the process of...

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 and Innovations for Poverty Action (IPA), but the practices listed below apply to all CAPI software...

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 checks can also be useful in improving your survey, identifying enumerator effects, and assessing the...

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 resource outlines the key steps involved in data processing and cleaning. We first describe best...