Skip to main content
M&E Studio
AI for M&E
AI How-TosPromptsRubricsPlaybooksPlugins
Indicators
Workflows
M&E Resources
M&E MethodsReference LibraryProposal Help
About
Services
FR — FrançaisES — Español
M&E Studio

AI for M&E. Built for the work you're already doing.

AI for M&E

  • AI How-Tos
  • Prompts
  • Playbooks
  • Plugins
  • Indicators
  • Workflows

M&E Resources

  • M&E Methods
  • Reference Library
  • Decision Guides
  • Tools
  • Courses

Company

  • About
  • Mission
  • Services
  • Contact
  • LinkedIn

Legal

  • Terms
  • Privacy
  • Accessibility

© 2026 Logic Lab LLC. All rights reserved.

  1. M&E Library/
  2. Topics/
  3. Evaluation
Topic Hub

Evaluation

Everything you need to design, commission, or manage an evaluation. From choosing the right type to writing a TOR to selecting methods and analyzing findings.

Evaluation Readiness Quiz
Assess whether your program is ready for evaluation across seven readiness factors
›
Data Collection Method Selector
Answer four questions and get recommended data collection methods matched to your needs
›
How to Draft Evaluation Reports with AI
Stop staring at a blank page. A 4-phase workflow turns your completed analysis into donor-ready evaluation narrative in hours, not days.
›

How Do I Choose?· 11

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 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.
How to Choose
How to Choose an Evaluation Methodology
A decision framework for choosing evaluation design. Covers experimental, quasi-experimental, and non-experimental approaches.
Decision Guide
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
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.
How to Choose
How to Write Evaluation Terms of Reference
A practical guide to writing evaluation TORs that get you a good evaluation. Scoping, evaluation questions, methodology expectations, timelines, budgets, and evaluator selection.
How to Choose
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.
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
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.
Comparison
The DAC Evaluation Criteria Explained
The six OECD-DAC evaluation criteria explained: what each means, which ones to use, and how to write evaluation questions for each.
Comparison

Reference Library· 29

In-Depth Guides (10)

In-Depth Guide
Contribution Analysis
A structured approach to building a credible case for how and why a program contributed to observed outcomes, without requiring experimental attribution.
In-Depth Guide
Developmental Evaluation
An evaluation approach designed for complex, adaptive programs in which goals and processes are emergent, and the evaluator works alongside the program team as an embedded learning partner.
In-Depth Guide
Impact Evaluation
A rigorous evaluation approach that measures the causal effect of a program on outcomes by comparing what happened with what would have happened in its absence.
In-Depth Guide
Most Significant Change
A participatory qualitative monitoring approach that systematically collects and selects stories of change to identify and share the most significant outcomes of a program.
In-Depth Guide
Outcome Harvesting
A retrospective evaluation approach that identifies, verifies, and analyses outcomes that have occurred, then determines whether and how the program contributed to them.
In-Depth Guide
Participatory Evaluation
An evaluation approach that actively involves stakeholders and beneficiaries throughout all stages, from design through use of findings, ensuring local ownership and relevance.
In-Depth Guide
Process Tracing
A within-case method for causal inference that tests whether the causal mechanisms predicted by a theory of change actually operated in a specific case, using systematic evidence to evaluate causal claims.
In-Depth Guide
Quasi-Experimental Design
A family of evaluation designs that estimate causal program effects without random assignment, using statistical methods to construct credible comparison groups.
In-Depth Guide
Realist Evaluation
An evaluation approach that asks what works, for whom, in what circumstances, and why, by identifying the mechanisms through which programs produce outcomes in specific contexts.
In-Depth Guide
Utilization-Focused Evaluation
An evaluation approach where every design decision is driven by the needs of the primary intended users, the specific people who will actually use the findings to make specific decisions.

Overviews (6)

Cost-Effectiveness Analysis
A systematic approach to comparing the costs and outcomes of alternative interventions to identify which delivers the best value for money in achieving specific objectives.
Evaluation Criteria (DAC)
The OECD-DAC framework provides five standard criteria, relevance, efficiency, effectiveness, impact, and sustainability, for systematically assessing the merit and value of development interventions.
Evaluation Matrix
A structured mapping document that links each evaluation question to its data sources, collection methods, indicators, and analysis approach, the operational blueprint for executing an evaluation.
Evaluation Terms of Reference
A formal document that defines the scope, objectives, methodology, and requirements for an evaluation, serving as the primary contract between the commissioning organization and the evaluation team.
Mixed Methods Evaluation
An evaluation approach that systematically combines quantitative and qualitative data to provide a more complete understanding of program effects, mechanisms, and context.
Rubric-Based Assessment
A structured evaluation approach using predefined criteria and performance levels to systematically assess programs, projects, or interventions against established standards.

Quick Reference (13)

Accountability EvaluationAudit vs EvaluationCompliance EvaluationEvaluation QuestionsEx-Ante vs Ex-Post Evaluation: Meaning and Key DifferencesFormative vs Summative EvaluationInception Report: What It Is and What to IncludeMeta-EvaluationPerformance EvaluationProcess Evaluation: What It Is and How to Conduct OneReal-Time EvaluationSustainability EvaluationSystematic Review

AI Guides· 2

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

AI Workflow
How to Draft Evaluation Reports with AI
Stop staring at a blank page. A 4-phase workflow turns your completed analysis into donor-ready evaluation narrative in hours, not days.
AI Workflow
How to Use AI for Baseline and Endline Analysis
Comparing baseline and endline data is the backbone of impact measurement. AI can run the comparisons, flag anomalies, and draft the narrative, but only if you structure the analysis around specific evaluation questions.

Explore Other Topics

Design
Theories of change, logframes, MEL plans, proposals, and design artifacts
24 entries · 8 guides
Data Collection & Quality
Methods, tools, DQAs, cleaning, and validation for field data
17 entries · 5 guides
Indicators
Select, design, track, and report on indicators
13 entries · 2 guides
Sampling
Sample size, sampling methods, design effect, and common mistakes
7 entries · 4 guides
Analysis
Quantitative and qualitative analysis, coding, statistics, and mixed methods
11 entries · 3 guides