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.
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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 and Access
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.
Pre-analysis plans
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...
Les plans de pré-analyse
Un plan de pré-analyse (PPA) décrit la manière dont les chercheurs prévoient d’analyser les données issues d’une évaluation aléatoire. Il est à distinguer de la notion de pré-enregistrement qui, en...
Randomization
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...
Randomisation
La randomisation à des fins d’inférence causale a une longue et riche histoire. Les expérimentations contrôlées randomisées ont été inventées par Charles Sanders Peirce et Joseph Jastrow en 1884. En...
Power calculations
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...
Calculs de puissance
Cette ressource décrit l’intuition qui sous-tend les calculs de puissance et fournit aux chercheurs des conseils pratiques et des exemples de code permettant d’effectuer ce type de calculs à l’aide de...
Introduction to measurement and indicators
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...
Introduction to measurement and indicators
Les mesures visent à obtenir des données fiables permettant de répondre aux questions de recherche et d’évaluer la validité de la théorie du changement. Si ces mesures sont imprécises, elles risquent...