M&E Guides
Learn the method, make a choice, or find the right M&E resource for proposal work. Practical guidance without requiring AI.
Browse M&E Guidance
Method guides explain how to do the work. Decision guides help you choose an approach.
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.
Decision guide · comparison
Common Sampling Mistakes in M&E
The eight sampling mistakes that undermine M&E data quality: wrong frames, ignored design effect, field substitution errors, and analysis overclaims.
Decision guide · how-to-choose
Custom vs Standard Indicators: Which to Use and When
Most M&E systems need both. Standard indicators satisfy donor compliance and comparability; custom indicators capture what is specific to your program. Here is how to mix them without doubling the reporting load.
Decision guide · how-to-choose
Design Effect Explained: What It Is and How to Apply It
Design effect (DEFF) is the correction that keeps cluster sample sizes honest. Here is what it is, the typical values in M&E, and how to apply it before fieldwork.
Decision guide · how-to-choose
Evaluation Ethics Checklist: What to Cover Before Fieldwork
Informed consent, confidentiality, risk minimization, ethics approval, and the specific protocols every evaluation needs before a single interview is conducted.
Decision guide · how-to-choose
How Much Should You Budget for M&E?
The 5-10% rule explained, evaluation cost ranges by type, budget breakdown templates, and how to negotiate when the M&E budget is too small for what the donor is asking.
Decision guide · how-to-choose
How to Choose an Evaluation Methodology: Matching Design to Question and Budget
A decision framework for choosing evaluation design. Covers experimental, quasi-experimental, and non-experimental approaches.
Decision guide · decision-tree
How to Choose Sample Size for M&E: The Five Factors, with Worked Examples
A practical guide to sample size for program evaluations, with rules of thumb, worked examples, and budget-statistics tradeoffs.
Decision guide · how-to-choose
How to Clean Your Dataset Before Analysis: A Step-by-Step M&E Checklist
A step-by-step checklist for cleaning M&E data after collection. Duplicate detection, outlier identification, skip logic validation, consistency checks, and cleaning log documentation.
Decision guide · how-to-choose
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.
Decision guide · how-to-choose
How to Design a Questionnaire for M&E: Seven Steps from the Analysis Plan Backward
A good questionnaire is the difference between data you can defend and data you cannot. Here is how to design one: from the analysis plan backward, through the pretest, to the version your enumerators can actually run.
Decision guide · how-to-choose
How to Verify AI Outputs for M&E
AI produces plausible-looking M&E content that often fails on inspection: fabricated statistics, hallucinated citations, broken logic, compliance gaps. Here is a verification checklist that catches the 7 most common failure modes before you ship.
Decision guide · how-to-choose
How to Write a Logframe: Step-by-Step Guide with Template
A logframe is not a filing form. It is the logic of your program written down so indicators, assumptions, and evidence can be tested against it. Here is how to write one: the four-by-four matrix, the right order to fill it, and the common mistakes that make most logframes unusable.
Decision guide · how-to-choose
How to Write a MEL Plan: A Practical Step-by-Step Guide
A MEL plan turns your program design into the measurement system that will run for 3-5 years. Here is how to write one that is specific, usable by field staff, and defensible to donors: the eight sections every plan needs, in the order to build them.
Decision guide · how-to-choose
How to Write a Theory of Change: Step-by-Step Guide
A theory of change is not a diagram. It is a written account of how your program produces change, including the assumptions that have to hold at each step. Here is how to write one: from long-term outcome backward, through assumptions, to activities.
Decision guide · how-to-choose
How to Write Donor Reports That Actually Get Read
How to write donor reports that get read. Indicator tables, narrative structure, explaining underperformance, and what donors actually look for.
Decision guide · how-to-choose
How to Write Evaluation Terms of Reference: The 8 Sections, with a Checklist
A practical guide to writing evaluation TORs that get you a good evaluation. Scoping, evaluation questions, methodology expectations, timelines, budgets, and evaluator selection.
Decision guide · how-to-choose
How to Write the M&E Section of a Proposal: The Seven Components
A step-by-step guide to writing the M&E, MEL, or MEAL section of a program proposal. What to include, how to structure it, and the mistakes that get proposals rejected.
Decision guide · how-to-choose
Indicator vs Target vs Milestone: What's the Difference?
Indicators, targets, and milestones are the building blocks of any MEL plan, but they're constantly confused. Here's how they relate, with examples from real programs.
Decision guide · comparison
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.
Decision guide · comparison
Logframe vs Theory of Change
Two frameworks everyone confuses. When you need a logframe, when you need a Theory of Change, why most programs need both, and which donors require which.
Decision guide · comparison
MEL vs M&E vs MEAL vs MLE: What's the Difference?
M&E, MEL, MEAL, MLE, DME: the acronym soup explained. What each stands for, which one to use, and why the terminology wars matter less than you think.
Decision guide · comparison
Outcome Harvesting vs Most Significant Change: Which to Use and When
Two participatory, theory-based evaluation methods that do not depend on counterfactual design. Outcome Harvesting works backward from observed outcomes; Most Significant Change works forward from participant stories. Here is when each wins and how they combine.
Decision guide · comparison
Output vs Outcome vs Impact: The Key Difference
The most common confusion in M&E. Learn the difference between outputs, outcomes, and impact with clear examples from health, education, and food security programs.
Decision guide · comparison
Paper vs Digital Data Collection: Which to Use and When
Digital is usually the right answer, but not always. Here is when paper still wins, when digital pays for itself, and the hybrid workflow that handles both.
Decision guide · comparison
Probability vs Non-Probability Sampling: When to Use Each
Probability vs non-probability sampling in M&E: when each approach is valid, which method fits your context, and five common mistakes that invalidate findings.
Decision guide · comparison
Process vs Outcome Indicators: What Each Measures and When to Use Them
Process indicators tell you whether the program is running as designed. Outcome indicators tell you whether it is producing the intended change. You need both. Here is how to pick the right mix, and how to avoid reporting only one while pretending to measure the other.
Decision guide · 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.
Decision guide · comparison
SMART Indicators: The Deep Dive
Most indicators fail SMART review because Specific and Measurable are vague. Here is how to apply the framework properly, with sector examples and the revisions that fix common mistakes.
Decision guide · how-to-choose
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.
Decision guide · comparison
The 5 Data Quality Dimensions for M&E
Validity, Reliability, Timeliness, Precision, Integrity. The five dimensions every DQA tests against, with the specific failures each one catches and how to fix them.
Decision guide · how-to-choose
WASH M&E: Activities, Standards, and Measurement Choices
A practitioner guide to monitoring and evaluation across the WASH sector: activity clusters, typical outputs and outcomes, collection methods, evaluation questions, and the standards that hold it together.
Decision guide · how-to-choose
Writing a Logframe for a Proposal with AI: The Workflow
AI can draft a defensible logframe in an hour if you feed it the right context. Here is the five-step workflow, what to hand the AI, and the review steps you cannot skip. Assumes you already have a theory of change; if not, start there.
Decision guide · how-to-choose
Cluster Sampling vs Stratified Sampling
Cluster sampling saves money when populations are spread out. Stratified sampling ensures subgroup comparisons. When to use each.
Decision guide · comparison
RCT vs Quasi-Experimental Design
When to use a randomized controlled trial vs a quasi-experimental design. Feasibility, cost, rigor, and what each can actually tell you about your program's impact.
Decision guide · comparison
The Mistake: Too Many Indicators in Your Proposal
Why packing your proposal logframe with 30+ indicators loses points with reviewers and creates an unfundable M&E budget. The fix is ruthless prioritization, not longer tables.
Decision guide · how-to-choose
Which DAC Criteria to Use: Choosing and Writing Evaluation Questions
The DAC criteria are a menu, not a checklist. How to choose which of the six to prioritize by evaluation purpose and timing, how to write sharp questions for each, and when humanitarian evaluations should use the ALNAP criteria instead.
Decision guide · comparison
Stuck on a Choice?
Decision Guides cover the trade-offs M&E teams hit most often: logframe vs theory of change, output vs outcome vs impact, and dozens more.
Browse Decision GuidesLooking for the AI Angle?
For step-by-step AI workflows on the same topics, head to AI for M&E.
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