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How Do I Choose?

Side-by-side comparisons and decision frameworks for the M&E choices that come up most often: logframe vs theory of change, output vs outcome vs impact, and more.

All comparison guides

  • Baseline vs Endline vs Midline Surveys Explained
  • Common Sampling Mistakes in M&E
  • Custom vs Standard Indicators: Which to Use and When
  • Design Effect Explained: What It Is and How to Apply It
  • Evaluation Ethics Checklist: What to Cover Before Fieldwork
  • How Much Should You Budget for M&E?
  • How to Choose an Evaluation Methodology: Matching Design to Question and Budget
  • How to Choose Sample Size for M&E: The Five Factors, with Worked Examples
  • How to Clean Your Dataset Before Analysis: A Step-by-Step M&E Checklist
  • How to Conduct a Data Quality Assessment
  • How to Design a Questionnaire for M&E: Seven Steps from the Analysis Plan Backward
  • How to Verify AI Outputs for M&E
  • How to Write a Logframe: Step-by-Step Guide with Template
  • How to Write a MEL Plan: A Practical Step-by-Step Guide
  • How to Write a Theory of Change: Step-by-Step Guide
  • How to Write Donor Reports That Actually Get Read
  • How to Write Evaluation Terms of Reference: The 8 Sections, with a Checklist
  • How to Write the M&E Section of a Proposal: The Seven Components
  • Indicator vs Target vs Milestone: What's the Difference?
  • KoboToolbox vs ODK vs SurveyCTO
  • Logframe vs Theory of Change
  • MEL vs M&E vs MEAL vs MLE: What's the Difference?
  • Outcome Harvesting vs Most Significant Change: Which to Use and When
  • Output vs Outcome vs Impact: The Key Difference
  • Paper vs Digital Data Collection: Which to Use and When
  • Probability vs Non-Probability Sampling: When to Use Each
  • Process vs Outcome Indicators: What Each Measures and When to Use Them
  • Qualitative vs Quantitative vs Mixed Methods
  • SMART Indicators: The Deep Dive
  • Surveys vs Interviews vs Focus Groups
  • The 5 Data Quality Dimensions for M&E
  • WASH M&E: Activities, Standards, and Measurement Choices
  • Writing a Logframe for a Proposal with AI: The Workflow
  • Choosing DAC Evaluation Criteria: Which of the Six to Use, and When
  • Cluster Sampling vs Stratified Sampling
  • RCT vs Quasi-Experimental Design
  • The Mistake: Too Many Indicators in Your Proposal

Looking for definitions instead?

The M&E Library has 150+ entries explaining frameworks, methods, and key concepts.

Browse the M&E Library