MEL Plan Template
Planning
Set out program outcomes, indicators, responsibilities, and a data plan in a guided MEL plan. Read more about MEL Plans
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Start with the work you need to do. Each guide explains the method and the decisions along the way.
Collecting data is only part of the work. This guide covers a decision-focused workflow: assessing data quality, cleaning datasets with an audit trail, building a structured learning agenda, and running data-to-decision sprints. Choose analysis methods suited to the questions, data, and evaluation design.
Choose methods for the evidence you need. MeStudio Forms offers prepared survey and key informant interview drafts that require contextual review, adaptation, and piloting before field use.
Poor data quality can limit the use of M&E findings. This guide introduces five practical quality lenses and a targeted assessment process. Confirm any applicable standard and required scope, and plan checks around the intended decisions and risks.
A monitoring, evaluation, and learning (MEL) plan is the document that ties your program's measurement activities together: what you'll track, how you'll collect data, and how you'll use it. This guide walks through the full development process.
A well-designed evaluation answers the right questions with the right evidence, without spending more than the program is worth. This guide covers evaluation types, design decisions, terms of reference, and the tools you need to get started.
Compare methods, frameworks and practical options before deciding how to proceed.
Find related concepts, guides and comparisons together.
Design, commission, and manage evaluations
45 resources
Theories of change, logframes, MEL plans, proposals, and design artifacts
34 resources
Methods, tools, DQAs, cleaning, and validation for field data
23 resources
Select, design, track, and report on indicators
15 resources
Sample size, sampling methods, design effect, and common mistakes
18 resources
Quantitative and qualitative analysis, coding, statistics, and mixed methods
23 resources
Start with the stage of the proposal, then use the resources that address the decision in front of you.
Write an M&E, MEL, or MEAL proposal section that answers the actual request, fits the submission stage, and promises feasible work.
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.
How to cost M&E activities first, a 5 to 10 percent rule of thumb and why sectors differ, illustrative evaluation cost ranges by type, budget breakdowns, and how to negotiate when the M&E budget is too small.
Section structure, indicator selection, baselines, disaggregation, budget feasibility, and alignment with the technical approach.
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.
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.
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.
A decision framework for choosing evaluation design. Covers experimental, quasi-experimental, and non-experimental approaches.
A structured explanation of how and why a set of activities is expected to lead to desired outcomes, mapping the causal logic from inputs to impact.
A structured matrix that summarizes a project's design, linking activities to expected results through a clear hierarchy of objectives with indicators, verification sources, and assumptions.
An articulation of expected results and cause-effect relationships for a project, program, or strategy, linked to indicators, assumptions, and monitoring.
A specific, observable, measurable variable that tracks progress toward an outcome or output.
Initial conditions data collected at the start of a project to establish a reference point for measuring change and setting indicator targets.
Changes in behavior, knowledge, skills, or conditions resulting from program outputs, experienced by beneficiaries.
Direct, tangible products of program activities; what the program produces, not what beneficiaries gain.
Long-term, higher-level effects attributable or contributed to by a program; broader change beyond individual outcomes.
A systematic process for checking whether data are fit for use, with explicit attention to validity, integrity, precision, reliability, timeliness, completeness, and accuracy.
Using multiple data sources, methods, or perspectives to cross-verify findings and strengthen the validity of evaluation conclusions.
The breakdown of aggregate data by sub-group characteristics, such as sex, age, location, or vulnerability status, to reveal inequities and differences in program reach and outcomes.
Free M&E plans, guides, checklists, and reports, with editable files and worked examples.
1 to 9 of 9 downloads
Planning
Set out program outcomes, indicators, responsibilities, and a data plan in a guided MEL plan. Read more about MEL Plans
Planning
Map the change a program seeks, its pathways, and the assumptions and external factors behind them. Read more about Theory of Change
Indicators
Define indicator measures, sources, disaggregation, collection methods, and targets for consistent reporting. Read more about Indicator Reference Sheet
Indicators
Track results against baselines and targets with yearly reporting sheets, a summary, and status counts. Read more about Indicator
Data Collection
Help field teams plan consistent interviews with question areas, interviewer notes, and a consent record. Read more about Key Informant Interviews
Data Collection
Help field teams record site visits with visit details, observer instructions, and a scored checklist. Read more about Observation Methods
Evaluation and Reporting
Agree evaluation scope and design before fieldwork, including questions, an evaluation matrix, and methods. Read more about Inception Report
Evaluation and Reporting
Connect evaluation questions to measures, sources, and methods, with coverage and triangulation checks. Read more about Evaluation Matrix
Evaluation and Reporting
Report results, activities, risks, lessons, and next steps to a donor or board, with space for indicator updates. Read more about Progress Report
Review and adapt each file before field use.
Search across reference material, general M&E guides, comparisons and definitions.
Showing 24 of 63 resources.
Decision Guides · Indicators
When you need baseline, midline, and endline surveys, what they collect, and what to do when you missed your baseline.
Decision Guides · Methods
Thematic, framework, content, grounded theory, narrative and rapid analysis answer different questions and carry different quality checks. Choose by question, time and stance, then use the checks that fit.
Decision Guides · Methods
Cluster sampling may reduce travel costs for dispersed populations. Stratification plans subgroup inclusion; useful comparisons also need adequate precision.
Decision Guides · Methods
Cochran's formula for sample size with step-by-step examples, the finite population correction, one rounding rule, and what to do when you need precision for each district.
Decision Guides · Methods
The eight sampling mistakes that undermine M&E data quality: wrong frames, ignored design effect, field substitution errors, and analysis overclaims.
Decision Guides · Evaluation
Three kinds of variable with different roles in the causal relationship between a program and its outcome. Which one you adjust for, which one you leave alone, and a causal diagram example.
Decision Guides · Evaluation
A controlled before-after study and difference-in-differences use the same four numbers. The difference is how you treat them. A two-by-two worked example.
Decision Guides · Indicators
Choose indicators for construct fit, applicable reporting obligations, comparability and cost. Standard, custom or mixed portfolios may be suitable; document definitions and any adaptations.
Decision Guides · Methods
Design effect (DEFF) is the correction that keeps cluster sample sizes honest. Here is what it is, the illustrative ranges in M&E, and how to apply it before fieldwork.
Decision Guides · Data Quality
A data quality assessment checklist: what to check in the office, what to check at the site, and what a red flag looks like. Includes a one-page field checklist and a filled example.
Decision Guides · Methods
A practical enumerator training checklist for survey teams: what to prepare, what to cover each day, and a short role-play script. Includes a training-day checklist and a worked example.
Decision Guides · Evaluation
Informed consent, confidentiality, risk minimization, ethics approval, and the specific protocols every evaluation needs before a single interview is conducted.
Decision Guides · Evaluation
A template and outline for writing an evaluation report, with a writing guide for each section and a filled example of findings, conclusions and recommendations that trace back to evidence.
Decision Guides · Evaluation
Separate what the evidence shows, what it means for the evaluation question, and what someone should do next, with a traceable worked example.
Decision Guides · Data Collection
A focus group discussion guide template: moderator script, consent text, ground rules, questions with probes, a note-taking outline and a worked example.
Decision Guides · Evaluation
Different questions about the same program need different designs. A worked case matches four questions to the evidence, timing and burden each one needs, and shows when to narrow a question.
Decision Guides · Data Collection
A fictional worked case follows six interviews from excerpts to codes, memos and claims, keeping the cases that do not fit and stating what the findings cannot say.
Decision Guides · Planning
How to cost M&E activities first, a 5 to 10 percent rule of thumb and why sectors differ, illustrative evaluation cost ranges by type, budget breakdowns, and how to negotiate when the M&E budget is too small.
Decision Guides · Evaluation
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 Guides · Evaluation
A decision framework for choosing evaluation design. Covers experimental, quasi-experimental, and non-experimental approaches.
Decision Guides · Methods
A practical guide to sample size for program evaluations, with rules of thumb, worked examples, and budget-statistics tradeoffs.
Decision Guides · Data Quality
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 Guides · Data Quality
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 Guides · Data Collection
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.
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