• Topics
    • Research Design
    • MEL
    • Research Ethics
    • Data Quality
    • Data Security
    • Data Collection
    • Data Cleaning
    • Data Science
    • Research Transparency
  • How-to Guides
  • Software Guides
    • Software Overview
    • The Shell
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    • GitHub
    • VS Code
    • Virtual Environments
    • Stata
    • Python
    • Quarto
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  • Contributing

Welcome to the IPA Knowledge Hub

Research, data science, and MEL resources curated by Innovations for Poverty Action

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Collecting data

Analyzing data

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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.

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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.

Browse MEL resources

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.

Read the ethics guidance

Data Quality

Implement validation, cleaning, and management practices to ensure data integrity. Covers quality assurance methods, error detection techniques, and protocols for maintaining reliable datasets.

Explore Data Quality protocols

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.

Review Data Security practices

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.

Browse Data Collection methods

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.

Explore Data Cleaning guides

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.

Discover Data Science techniques

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.

Learn about Research Transparency

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.

Browse the Software Guides

 
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