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  1. M&E Library
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  5. Data Collection
Topic Hub

Data Collection

Data collection is where M&E plans meet reality. The best logframe in the world means nothing if the data it depends on is poorly collected, too expensive to gather, or arrives too late to inform decisions. This hub covers methods (surveys, interviews, focus groups, observation), sampling (how many people, chosen how), technology (mobile platforms, offline capability), and the practical decisions that determine whether your data is trustworthy and useful.

How Do I Choose?

Side-by-side comparisons, decision trees, and practical guidance for common M&E decisions.

Baseline vs Endline vs Midline Surveys Explained
When you need baseline, midline, and endline surveys, what they collect, and what to do when you missed your baseline.
Comparison
How to Choose Sample Size for M&E
A practical guide to sample size for program evaluations, with rules of thumb, worked examples, and budget-statistics tradeoffs.
How to Choose
KoboToolbox vs ODK vs SurveyCTO
The three most common mobile data collection platforms for M&E, compared on features, cost, offline capability, skip logic, and hosting. Plus CommCare for case management.
Comparison
Qualitative vs Quantitative vs Mixed Methods
Qualitative, quantitative, and mixed methods are not a quality ranking. They answer different questions. Here's when to use each, how to combine them, and what integration actually looks like.
Comparison
Surveys vs Interviews vs Focus Groups
The three most common M&E data collection methods, compared. Surveys tell you how many, interviews tell you why, focus groups tell you what people agree on.
Comparison

Interactive Tools

Sampling Calculator
Calculate the sample size you need for surveys, evaluations, and monitoring activities

Reference Library(15 entries)

Overviews

Baseline Design
A structured approach to collecting initial condition data that directly informs project decisions, minimizes burden, and enables valid comparison with endline measurements.
Data Collection Burden
The total time, effort, and resources required from respondents and implementers to complete data collection activities, balanced against data quality needs and programme capacity.
Data Management
The systematic processes for collecting, storing, securing, and maintaining data quality throughout the data lifecycle to ensure information is accurate, accessible, and usable for decision-making.
Data Quality Assurance
A systematic process for verifying that collected data meets five quality dimensions, Validity, Integrity, Precision, Reliability, and Timeliness, ensuring data is fit for decision-making.
Focus Group Discussions
A qualitative data collection method that brings together 6-10 participants to discuss a specific topic, generating rich insights through group interaction and shared experiences.
Key Informant Interviews
In-depth, semi-structured interviews with individuals selected for their specific knowledge, experience, or perspectives relevant to the evaluation questions.
Observation Methods
A systematic approach to collecting data by directly watching and recording behaviours, interactions, and processes as they occur in natural settings.
Sampling Methods
Systematic approaches for selecting a subset of a population to represent the whole, balancing statistical validity with practical constraints.
Survey Design
The process of designing structured questionnaires and survey protocols to collect reliable, valid, and actionable data from a defined population.

Quick Reference

Census vs SampleCluster SamplingMidlinePurposive SamplingQualitative DataQuantitative Data

AI Guides

How to Build Better Surveys with AI
Most AI survey tools stop at generating questions. This guide covers the full lifecycle: choosing question types, catching bias, adding skip logic, and piloting before you deploy.
How to Clean Messy M&E Data with AI
Turn 15 hours of manual cleaning into 2 with a 4-step workflow that combines free tools and AI validation to catch errors human review misses.
How to Protect Data Privacy When Using AI for M&E
Beneficiary data belongs to beneficiaries, not AI servers. The SAFE Framework helps you use AI tools without risking a data protection breach, donor compliance violation, or loss of community trust.

Explore Other Topics

Evaluation
Design, commission, and manage evaluations
MEL Design
Theories of change, logframes, results frameworks, and logic models
Indicators
Select, design, track, and report on indicators
Data Quality
Ensure trustworthy data from collection to analysis
Sampling
Sample size, sampling methods, design effect, and common mistakes