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  1. M&E Library/
  2. Topics/
  3. Data Collection & Quality
Topic Hub

Data Collection & Quality

Methods, tools, and sampling approaches for collecting M&E data in the field, plus the quality assurance practices that keep data trustworthy through analysis. Surveys, interviews, focus groups, mobile tools, DQAs, cleaning, and validation.

DQA Scorecard
Score your data quality across five standard dimensions with rubric-based assessment
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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.
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How Do I Choose?· 5

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

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 Conduct a Data Quality Assessment
A step-by-step guide to conducting a DQA using the five standard dimensions. How to select indicators, design verification procedures, conduct the site visit, and write the DQA report.
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

Reference Library· 17

Overviews (8)

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 program 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 behaviors, interactions, and processes as they occur in natural settings.
Survey Design
The process of designing structured questionnaires and survey protocols to collect reliable, valid, and actionable data from a defined population.

Quick Reference (9)

BiasCensus vs Sample: When to Use Each in M&EMidlinePrimary vs Secondary DataQualitative DataQuantitative DataReliabilityTriangulationValidity (Internal & External)

AI Guides· 3

Step-by-step workflows for using AI in your M&E work.

AI Workflow
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.
AI Workflow
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.
AI Workflow
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.

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