Research Design
Design research grounded in best practices for measuring impact. Covers sampling strategies, randomization methods, power calculations, measurement frameworks, and implementation best practices for RCTs and other research designs.
MEL
Monitoring, Evaluation, and Learning guidance to help organizations build data-driven learning. Includes A/B testing, theory of change development, and practical resources for continuous learning based on right-fit approaches to data and methods.
Research Ethics
Plan for and navigate ethical considerations including informed consent, data privacy, and IRB processes. Provides guidance on ethical compliance, human subjects protection, and institutional review requirements.
Data Quality
Follow IPA’s Research Protocols, the “Minimum Must Dos” every IPA project must meet. Covers survey planning, bench testing, piloting, accompaniments, high frequency checks, backchecks, and the Data Management System that supports them.
Data Security
Protect sensitive research data and respondent confidentiality. Covers PII handling, encryption protocols, device security, and compliance with IRB and legal requirements across the research data lifecycle.
Data Collection
Learn digital and field data collection methods using modern tools and techniques. Provides strategies for designing efficient, reliable data collection systems and managing field operations.
Data Cleaning
Address common data issues including missing values, outliers, and inconsistencies. Offers practical guides, code examples, and systematic approaches for preparing data for analysis.
Data Science
Introduces techniques for web scraping, data analysis, and visualization. Covers statistical and econometric methods, machine learning basics, and best practices for deriving insights from data.
Research Transparency
Make research verifiable and reusable through open science practices. Covers transparency principles, preparing data and code for public sharing, and publishing replication packages to IPA’s Dataverse.
Software Guides
Learn essential tools for research, data science, and MEL projects. Provides tutorials and best practices for Stata, Python, R, and other software commonly used in research, evaluation, analysis, and software development.