What is a logframe?
A logframe, or logical framework matrix, is a four-by-four table that sets out what a program intends to achieve, how each result will be measured, where each number will come from and which conditions must hold. It puts the chain of results and the measurement plan on one page.
The rows run from activities up through outputs and purpose to the goal; the columns hold a narrative summary, indicators, means of verification and assumptions. Funders vary the labels and often add baseline and target columns.
THE SHORT VERSION
- A logframe is the same change model as a theory of change or logic model, laid out as a matrix with an indicator, a source and an assumption beside each level.
- The means of verification column names where each number comes from, so it decides whether a reported number can be trusted.
- When reports arrive, read each one against the logframe row by row and turn every gap into one question for the funded partner.
What are the rows and columns of a logframe matrix?
The four rows are levels of results (goal, purpose, outputs, activities), and the four columns say what each result is, how you will know, where the evidence comes from and what must be true. Read across a row to see how one result is measured.
| Column | Question it answers | Common mistake |
|---|---|---|
| Narrative summary | What is this result, in plain words? | An activity written where a change belongs |
| Indicator | How will we know it happened? | A label with no definition, such as “participants supported” |
| Means of verification | Which form or record produces the number? | “Project report”, which names no source |
| Assumptions | What outside our control must hold for the next level? | Restating the goal: “the program succeeds” |
Some formats add an inputs row below activities, often for the budget. Keep detailed definitions outside the matrix so it stays readable.
How do you read the vertical and horizontal logic of a logframe?
Vertical logic asks whether each level plausibly leads to the one above it once the assumptions hold; horizontal logic asks whether each row's indicator and source can actually show that result. Test both before anyone signs the matrix.
Read vertically as if-then statements: if the activities happen and that row's assumption holds, the outputs follow. Read horizontally row by row: a completion register verifies an output, not a job, so a weak source means a better source or a narrower claim.
What does a completed logframe look like?
Here is the logframe for a fictional regional workforce fund that supports four job-training partners, built from the change model agreed on its onboarding call. The same example runs through Sopact's free course on measurement and reporting.
| Level and narrative | Indicator | Means of verification | Assumption |
|---|---|---|---|
| Goal: graduates in living-wage jobs | Starting wage, hourly, by track | Placement survey | Suitable jobs stay within reach |
| Purpose: graduates placed and still employed | Placed within 90 days of exit; retained in the same job at 12 months | Placement survey; 12-month follow-up | The job fits the person, so they stay |
| Outputs: trained, certified participants | Enrolled (unique people, not visits); completed training | Enrollment form; completion record | Employers recognize the certification |
| Activities: training and mentoring | Not in the reporting agreement | Program records | Content matches what local employers hire for |
All five metrics from the fund's reporting agreement appear: two outputs, two purpose-level outcomes and starting wage by track at the goal. The activities row reports nothing to the funder, because the fund agreed to track change, not effort.
The short video below argues that most logframes break because they are filled in for the proposal and then shelved. Watch for the moment the matrix is tied to the records that feed each indicator.
How do you write an objectively verifiable indicator?
An objectively verifiable indicator says exactly what will be counted or observed, for whom and over what window, so that two people counting the same records get the same number. “Objectively verifiable” does not remove judgment; it makes the judgment visible.
| Weak wording | More usable wording | Still needed |
|---|---|---|
| Graduates find work | Placed in a job within 90 days of exit, counted as unique people | Which exit date; what counts as a job |
| Participants are job-ready | Share meeting the agreed task-assessment standard at completion | Task, rubric, assessors, denominator |
| Partners report on time | Share of quarterly submissions received by the agreed deadline | What counts as a complete submission |
Set a baseline, a target with a reason and milestones where they help; a target is a commitment, not evidence. The UK government's revised logframe guidance keeps them in separate fields for that reason.
Each indicator also needs one written definition both sides apply. In the workforce example, “enrolled” means unique trainees in the reporting period, not sessions; that line is what lets four partners' numbers be added.
What should the means of verification column say?
The means of verification names the record each number comes from, who collects it and when, so anyone can trace a reported figure back to its source. It is the column teams rush in the proposal and need most when reports arrive.
“Project report” is not a source; it is where the number ends up. Name the form or record underneath: the enrollment form for enrolled, the placement survey for placed within 90 days, the 12-month follow-up for retention. For an outside statistic, check that its population, geography and timing match your indicator.
This is where one ID per person matters: when enrollment, placement and follow-up sit on the same participant record, the source is a record you can open. In Sopact Sense each participant keeps one ID from the first form, and every line of an AI Assistant answer links to its record.
What belongs in the assumptions column?
Assumptions are conditions outside the program's control that must hold for one level to lead to the next, written specifically enough to monitor. “Stakeholders remain supportive” is weaker than naming the employer commitment the placement row depends on.
Separate an assumption from a delivery task. If your team arranges transport, that belongs in the workplan; if attendance depends on a bus route staying open, it is an assumption. Give the important ones an owner and a review date, and when one fails, record the consequence rather than quietly rewriting the plan.
How is a logframe different from a theory of change, logic model or results framework?
It is the same change model in a different layout: a logframe calls outcomes and impact “purpose” and “goal”, and adds an indicator, a source and an assumption beside each level. A theory of change gives more room to why each step should follow, a logic model to delivery, and a results framework reads down from a strategic objective.
The Logical Framework Approach is the planning process behind the matrix; the matrix is one output of it. Build the model once and lay it out for each reader, as the chapter One change model, four formats shows, with our theory of change guide for the reasoning behind each arrow.
How do you check a partner report against a logframe?
Read the report against the logframe, not the other way round: for each indicator, find the reported number, compare its definition and source with the matrix, and give it one status. The four statuses are matches, different, missing and not split, and only a match can join a portfolio total as it is.
In the workforce example, Partner C reports 80 people enrolled, counted as unique people, so that row matches. It counts placements within six months instead of 90 days, leaves out 12-month retention and gives one average starting wage instead of one per track.
None of the gaps says anything about performance; they say the number cannot be compared yet. Send the questions together within days, as the chapter Check each partner report against the agreement describes. An AI tool can do the first read if you forbid guessing.
PROMPT · PASTE INTO CLAUDE, CHATGPT OR YOUR AI TOOL
You are checking a funded partner's report against our logframe. Our logframe (per level: narrative, indicator with definition, means of verification, assumption): [PASTE LOGFRAME] The partner's report: [PASTE OR ATTACH THE REPORT] For each indicator, in order: 1. Quote the reported number and where it appears. 2. Say whether it matches our definition and our named source. 3. Give one status: matches, different, missing or not split. 4. For anything but a match, draft one short question the partner can answer from its records. Rules: do not invent, estimate or convert any number; if the report does not give it, write "not in our data". Do not treat a similar indicator as a match. List any assumption the report says did not hold.
What can a logframe not tell you?
A logframe is a plan for evidence, not evidence of change: a filled matrix does not show that outputs became outcomes, or that the program caused them. Placement and retention also depend on employers and on each graduate's life.
A rise in placements is not proof of cause without a comparison, such as earlier cohorts or an outside figure on the same definition. Follow-up relies on self-report and on who answers; non-responders are unknown, not failures. A person checks any AI draft against the record and decides what it means.
How do you build your first logframe? Start with one program
Start with one program and one funder's reporting agreement, and fill the matrix from the top down before adding any indicator you cannot source.
- Write the goal and the purpose in one line each, naming who changes.
- Work down to outputs and activities, and write one assumption for each link between levels.
- Place each agreed metric on its level as an indicator, with a one-line definition such as unique people or a 90-day window.
- Name the form or record behind every indicator, who collects it and when.
- When the first report arrives, read it against the matrix row by row and send one question per gap.
After the first reporting cycle you have a signed matrix, a definition and a named source for every indicator, and a list of gaps to fix next year. For a copyable matrix, see the logframe template; for a fully worked one, the logframe example.
Frequently asked questions
What are the four columns of a logframe?
The usual four are the narrative summary, objectively verifiable indicators, means of verification and assumptions. Together they say what each result is, how you will know, which record produces the number and what must hold for the next level. Many funders split the indicator column into baseline, target and milestones; use their layout, but keep a definition and a named source behind every indicator.
What is the difference between purpose and goal in a logframe?
The purpose is the main change the program itself works toward, one level above its outputs; the goal is the wider change it contributes to but does not control. In the workforce example, the purpose is graduates placed within 90 days and still employed at 12 months, and the goal is living-wage jobs.
What is an objectively verifiable indicator?
It is an indicator defined precisely enough that two people counting the same records reach the same number. It names what is counted, for whom and over what window, such as “placed in a job within 90 days of exit, counted as unique people”. Judgment does not disappear; the definition and evidence can be checked by someone else.
Can a logframe change during implementation?
Yes, when the evidence justifies it and the funder agrees through its process. Keep the earlier matrix with the reports that used it, and record what changed, why and who approved it. A changed definition, such as a new placement window, usually means earlier figures cannot be added to later ones.
Does a completed logframe prove impact?
No. It organizes the plan and says what evidence will be collected. Meeting an indicator shows that a defined result happened among the people you counted, not that the program caused it. A causal claim needs a comparison chosen in advance, such as earlier cohorts or an outside figure on the same definition, and an account of who did not respond.
