What is a logframe template?
A logframe template is a structured table for describing a program's intended results, how progress will be assessed, where evidence will come from and which assumptions matter. It commonly connects a goal, outcomes, outputs and activities with indicators and means of verification. Formats vary by funder and organization.
The template is useful during planning as well as delivery. It can reveal an unclear outcome, an unrealistic evidence request or an assumption that needs discussion before any data exists. A completed table is not proof that the proposed causal pathway will work.
This guide includes a blank spreadsheet-ready template, instructions for each field and a way to keep the plan connected to later review. If you want to see a completed matrix first, open the worked logframe example.
Download the editable logframe template
Download the blank logframe template — CSV for Excel or Google Sheets →
The file contains column headings and starter rows for goal, outcome, output and activity. Open or import it in a spreadsheet, replace the starter labels and add rows as needed. Save your working copy in your preferred spreadsheet format. This is a practical planning template, not a funder-approved form.
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| Result level | What to write | Evidence question | Assumption question |
|---|---|---|---|
| Goal or impact | The broader change the program intends to contribute to | What suitable evidence could describe that change and context? | What external conditions affect the contribution? |
| Outcome | A relevant change in behavior, capacity, conditions or practice | How will this particular change be observed? | What must hold between delivery and the intended change? |
| Output | The product, service or capacity directly delivered | Which delivery or quality record will confirm it? | What conditions affect use or uptake? |
| Activity | The work undertaken to produce outputs | Which operational record shows implementation? | What resources or conditions are necessary? |
The downloadable version adds fields for an indicator definition, baseline and date, target and date, source, collection timing, owner, assumptions and review notes. Some formal templates put resources or costs in the activity row instead of indicators. Adapt the structure to the actual requirements.
Start with the result chain, not a list of numbers
Describe the problem and the intended change with the people responsible for the work and, where appropriate, those affected by it. Then ask whether the proposed activities can plausibly produce the outputs and whether those outputs could support the intended outcomes.
A theory of change can explain the reasoning and assumptions in more detail. A logic model can display the relationships. The logframe provides a structured planning and monitoring reference; it should not replace the discussion behind those relationships.
The IFRC planning manual is a useful methodological reference. The European Commission's logical framework guidance also connects results, assumptions and indicators. Use the applicable format when a funder specifies one.
How to fill in the logframe template
1. Describe one meaningful result per row
Write an outcome as a change, not merely an activity completed. “Participants use a taught practice in their work” is different from “run workshops.” Keep the population and time horizon clear enough that the team can decide what evidence would be useful.
2. Define the indicator
An indicator should explain what is observed and how it is interpreted. For a percentage, define the numerator and denominator. For a count, define the unit and duplicate handling. For a qualitative assessment, define the method and what evidence supports the judgment.
One result may need more than one indicator. Do not force a complex outcome into a single number merely to keep the table small. Conversely, do not add indicators that have no intended decision or reporting use.
3. Record the baseline honestly
A baseline is the relevant starting value or condition. If it is unknown, record that and plan how it will be established. Do not enter zero simply because nothing has been measured yet.
Some delivery measures legitimately start at zero for a new activity; an outcome such as existing skill use may not. The baseline source and date matter as much as the number.
4. Set the target and observation date
A target is an intended result, not an observed achievement. Explain its basis using relevant evidence, program capacity and context. Where the evidence is not yet sufficient, record a provisional target or the process for setting it rather than inventing certainty.
5. Specify means of verification
Name the source and method that will support the indicator: an attendance record, operational dataset, assessment, suitable survey, document review or another appropriate source. “Program report” is too vague if nobody knows how the report's figure will be produced.
Not every measure comes from a participant response. A program-level output might use delivery records, while a broader goal may use secondary statistics or specialist evaluation. A source plan should include timing, access and important limitations.
6. Make assumptions explicit
Record the conditions that affect the proposed links, including those outside the team's direct control. “Participants have an opportunity to use the skill” is a meaningful assumption for a training outcome. “The program succeeds” merely repeats the desired result.
Consider what would happen if an assumption failed. The response may be a design change, further inquiry or an explicit limit on the claim—not a more confident target.
7. Assign an owner and a review point
Name who collects the evidence, who checks it and when the result will be reviewed. Use a cadence suited to the activity and expected outcome. Not every outcome can be measured continuously, and repeated collection can create unnecessary burden.
Example of a more usable indicator definition
Fictional planning example. A professional-development program wants to understand whether participants use a taught practice after the course.
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| Field | Illustrative entry |
|---|---|
| Outcome | Participants use the practice in a relevant work setting within three months |
| Indicator | Number and percentage of eligible follow-up respondents reporting defined use |
| Coverage measure | Respondents divided by the eligible follow-up cohort |
| Definition | State which practice, what counts as use and the observation window before collecting |
| Baseline | Establish existing practice at entry; do not assume zero |
| Source | Appropriate follow-up questions, with optional contextual evidence where justified |
| Assumption | Participants have a suitable opportunity to apply the practice |
| Limit | Self-report and nonresponse constrain interpretation; this does not isolate causal impact |
A target for this indicator should be chosen using the program's actual evidence and capacity. The wording above is an example, not a validated scale or universal benchmark.
Use the same core definitions across local programs
Different sites may deliver different activities and need different questions. Agree the small shared core required for the comparisons that matter, then map local collection to those definitions. The logframe can reference the dictionary rather than becoming an oversized questionnaire.
Retain appropriate organization, program and reporting-period context. Person-level linkage may be useful for an individual-change question, but anonymous or group-level evidence can be appropriate for other purposes. Choose the data structure from the question.
Before aggregating, check that units, eligibility, periods and methods match. Do not add “people reached” from one site to attendance entries from another and label the total unique participants.
Keep the template useful during delivery
Add observed results to a separate monitoring view or clearly identified result column. Keep the original target distinct from actual performance. For each review, record evidence coverage, interpretation and the next action.
- Target met: Check the definition and evidence before making the claim.
- Target not met: Investigate the result and assumptions without hiding the gap.
- Evidence incomplete: State what is unknown; missing information is not automatic failure.
- Definition changed: Preserve the version and explain whether comparison remains valid.
A reviewed static logframe can be legitimate. What matters is whether it supports the required decisions and has a maintained connection to evidence. Software does not need to update every cell in real time to make the plan useful.
Common logframe mistakes
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| Mistake | Better approach |
|---|---|
| Calling a workshop an outcome | Separate delivery from the intended later change |
| Using zero for an unknown baseline | Mark it unknown and assign a collection plan |
| Entering a target as an achieved result | Keep targets and observations in different fields |
| Writing “survey” as the full verification plan | Define population, question, timing, method and coverage |
| Claiming the pathway is proven because indicators improved | Consider other explanations and the appropriate evaluation design |
| Changing a measure without a record | Version the definition and document historical treatment |
Where connected collection and analysis helps
A spreadsheet can be a good planning tool. The recurring burden appears when definitions, collection, comments and reported results drift apart and the team rebuilds their connections at each review.
Sopact supports connected collection, quantitative context and reviewed qualitative analysis. The operating team owns the definitions and judgments. In codebook-based work, automated processing can apply reviewed categories to eligible responses and support reruns after revisions, while reviewers examine the evidence and uncertainty.
The useful test is one real indicator: can the team inspect its definition, population, calculation, source and current limitations, then make a controlled correction? Do not assume software proves the causal pathway or makes every source appropriate.
For the staff-effort comparison, see the qualitative and quantitative analysis guide. Include setup, collection and review in any ownership estimate.
Turn the reviewed logframe into a clear report
Report the result, period, source and important limitation alongside the target. Explain what the team learned and what changes next. A missed target or incomplete follow-up deserves an honest account, not a rewritten objective.
Use How to Write an Impact Report and report examples. For the broader method, continue to impact measurement and outcome evaluation.
Watch: keep the logframe connected to evidence
This video discusses the problem of a planning matrix becoming disconnected from evidence. Apply the idea using suitable sources, review timing and limits on interpretation.
Frequently asked questions
Can I edit the logframe template in Excel?
Yes. Download the CSV and open or import it in Excel or Google Sheets. Replace the starter rows and save your working copy in your preferred spreadsheet format.
Does every logframe have the same columns?
No. Formats vary. Use the required funder or organizational template where applicable and retain enough detail to define results, evidence and assumptions.
Should an unknown baseline be zero?
No. Zero is a value, not a synonym for unknown. Record the gap and how the baseline will be established.
Does every indicator require a participant survey?
No. Delivery records, operational data, documents, assessments and secondary sources may be appropriate. Choose the source from the indicator and its purpose.
Does meeting targets prove the theory of change?
No. Target attainment is relevant evidence, but it does not establish every causal link or rule out other explanations.
How often should I update the logframe?
Review it at intervals suited to decisions and expected results. Document changes and follow any applicable approval process. Continuous collection is not necessary for every measure.
Choose your next step
King Center
Related practice: bringing program feedback into review. This is not a validation of the fictional planning example or a funder-approved logframe.
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