What does a useful logframe example look like?
A worked logframe example shows the proposed result chain alongside indicators, evidence sources and assumptions. It makes the planning choices concrete: what will be delivered, what change is expected, how it will be observed and what could prevent the link from holding.
The example below follows a fictional professional-development program. It includes a completed planning matrix and a later review using invented results. The figures illustrate the method; they are not customer outcomes, validated benchmarks or recommended targets for your program.
To build your own, use the editable logframe template and instructions. This page focuses on reading and evaluating a filled example.
The program in this example
A professional network offers a short course to 100 enrolled participants. The course teaches a defined workplace practice and includes four workshops. The team wants participants to apply that practice in a relevant setting, not simply finish the sessions.
The proposed chain is:
- Activities: prepare and deliver the workshops with opportunities to practise.
- Output: participants complete the defined course requirements.
- Outcome: participants use the taught practice in a relevant work setting within three months.
- Broader contribution: more effective professional practice over time.
The final link needs more than a course-completion count. Opportunities to apply the practice, organizational support and the wider work environment can affect the outcome. The logframe should expose those assumptions rather than make the program appear to control them.
Completed logframe example
Download the fictional worked example — CSV for Excel or Google Sheets →
Scroll horizontally to see all columns →
| Level and result | Indicator and planned target | Means of verification | Assumptions and limits |
|---|---|---|---|
| Goal: contribute to more effective professional practice | Relevant evidence of sustained practice and its usefulness at a later review; select a suitable method before setting a numeric target | A planned longer-term assessment using appropriate participant and organizational evidence | Other influences affect professional performance; the course alone cannot be assumed to cause the wider change |
| Outcome: participants apply the taught practice within three months | Target: 60 of the 100 enrolled participants report defined use; separately seek follow-up from at least 80 of the 100 | A defined three-month follow-up instrument linked appropriately to the enrolled cohort, with coverage and source status retained | Participants have an opportunity to apply the practice; reported use is not independent verification or proof of attribution |
| Output: participants complete the course | Target: 80 of 100 enrolled participants meet the pre-defined completion requirements | Completion records checked against the course requirements | Participants can access the sessions and required activities; completion is not proof of later application |
| Activity: deliver the learning sessions | Target: four workshops delivered by the end of month two | Delivery schedule and session records | Facilitators, materials and accessible participation arrangements are available |
The longer-term goal is intentionally not assigned a made-up numerical target. The planning team must choose a meaningful measure and feasible evidence before committing to one. A transparent unresolved design choice is better than a decorative target.
The example uses a common logframe structure, but formal formats vary. The IFRC planning manual and European Commission's logical framework guidance are useful methodological references. Follow the applicable funder format where one is required.
What happens to the baseline?
The delivery baseline is zero workshops for this new course cycle. That does not mean the participants begin with zero relevant skill or practice. The program needs to establish existing practice at entry if it wants to describe individual change later.
For the outcome above, the target concerns reported use after the course, not an increase from an assumed baseline. If the team wants to claim an increase, it needs a comparable starting observation, suitable matching and a clear analysis plan. Even then, change alone does not establish the course's causal effect.
Record “not yet established” for an unknown baseline and assign responsibility for obtaining it. Do not use zero to make the matrix look complete.
The outcome indicator needs more detail than one sentence
The matrix is a summary. Its outcome row should link to a definition the collection and reporting teams can apply consistently.
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| Definition field | Entry for this fictional example |
|---|---|
| Eligible population | The 100 participants enrolled in the specified course cycle |
| Reported use | A response meeting the program's pre-defined description of using the taught practice in a relevant setting |
| Observation window | Three months after the course, with an agreed collection window |
| Target denominator | All 100 enrolled participants, not only those who respond |
| Coverage | Number answering follow-up divided by the eligible cohort |
| Missingness | No response remains unknown; it is not automatically no use |
| Source status | Participant self-report unless a separately defined verification method is used |
| Owner | The program team collects; a designated reviewer checks the result and wording |
Use custom questions appropriate to the practice and population. The example is not a validated questionnaire. If a validated instrument is needed, use it within its intended scope and scoring guidance.
Now review the example using actual observations
Fictional review results. The team delivers four workshops. Eighty-four participants complete the course. Sixty answer the follow-up and 36 report using the practice under the agreed definition.
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| Measure | Plan | Observed result | What the reviewer can say |
|---|---|---|---|
| Workshops delivered | 4 | 4 | The delivery target was met under the defined records |
| Course completion | 80 of 100 | 84 of 100 | The completion target was exceeded |
| Follow-up coverage | 80 of 100 | 60 of 100 | Coverage was below the intended level |
| Known reported use | 60 of 100 | 36 of 100 | The recorded evidence does not demonstrate the planned level; outcomes for 40 nonrespondents remain unknown |
Among respondents, 36 ÷ 60 = 60% reported use. Across the enrolled cohort, 36 ÷ 100 = 36% have a recorded report of use. The team must not substitute the respondent percentage for the original target denominator and declare the target met.
The opposite overstatement is also wrong: missing follow-up does not prove that 64 participants did not use the practice. Preserve uncertainty and consider whether additional appropriate follow-up is feasible.
Use comments to examine the assumptions
Suppose 12 respondents describe a lack of opportunities to apply the practice. That finding suggests a useful question for the program team: should future delivery include more work with employers or local managers?
Keep the question asked, the eligible comments and the coding definition with the theme. Do not assume those 12 responses explain every missing outcome. Some participants may describe other barriers, and nonrespondents may differ from respondents.
In reviewed codebook-based analysis, the team defines categories and checks ambiguous responses. Automated application and reruns can reduce repeated coding work while quantitative context remains connected. The qualitative and quantitative analysis guide shows that workflow and an illustrative staff-hours comparison.
What would a revised plan look like?
The team might test an application activity with one participating organization, clarify follow-up timing and examine whether the taught practice fits participants' roles. These are possible responses to the evidence, not proven solutions.
Record the decision, owner and review date. If the outcome definition or target changes, preserve the original version and explain the revision. Do not silently rewrite the earlier plan to match the observed result.
Some funder agreements require formal approval for changes. Follow the applicable process. The value of review is a better-informed decision, not an excuse to remove accountability.
How to adapt the example to another topic
Scroll horizontally to see all columns →
| Context | Possible output | Possible outcome question | Source issue to resolve |
|---|---|---|---|
| Member network | Chapters submit a defined annual return | Can chapters use the returned analysis for a local decision? | Different local forms need a limited shared dictionary |
| Service program | Appropriate referrals are made | Do participants access useful support? | A referral sent is different from support received |
| Partner development | Partners complete an agreed improvement activity | Is the intended practice implemented and maintained? | A self-reported action may need supporting evidence |
These are starting points, not universal indicators. Choose the result and source with the people responsible for and affected by the work. Do not copy the training numbers into another sector.
Evidence is broader than one participant record
Some indicators use delivery logs, organizational records, documents or secondary statistics. Others use participant feedback or repeated observations. The source should fit the question and be accessible through an appropriate review process.
For federated programs, agree only the common fields needed for valid comparison and allow additional local collection. Keep units, populations, periods and methods explicit. A common outcome label does not justify adding incompatible numbers.
Where personal linkage is appropriate, govern the identifiers and access. Where anonymous or aggregate evidence is sufficient, do not collect unnecessary identity merely to make every row trace to a named person.
How to move from the example to an operating workflow
A spreadsheet can hold a sound logframe and its source references. The work becomes harder when new data arrives repeatedly, definitions change and qualitative findings must be joined to quantitative results for each review.
Sopact supports connected collection, analysis and governance around relevant records and definitions. Test one indicator from collection through calculation and review: can staff explain the denominator, open the permitted source, inspect an exception and preserve a corrected version?
The software supports the evidence process. It does not establish that the logframe's assumptions are true or that the program caused the observed change. Keep method choices and interpretation with accountable people.
Build your own and report it clearly
Download the blank logframe template, then draft one outcome and its evidence plan before expanding the matrix. Use theory of change to examine the reasoning and outcome evaluation to select a suitable assessment approach.
When reporting, distinguish planned targets, observed results, assumptions and unknowns. Use How to Write an Impact Report and report examples for practical presentation guidance.
Watch: connect the logframe to evidence
This companion video discusses why a logframe can become disconnected from measurement. Use the example above to turn that concern into specific definitions, sources and review decisions.
Frequently asked questions
Is this logframe example from a real customer?
No. The program, targets and observed results are fictional teaching examples. They are not benchmarks or customer claims.
How is an example different from a template?
An example shows a filled matrix and how to interpret it. A template provides blank fields to adapt to your own program and requirements.
Does every logframe row need a numeric target immediately?
Do not invent a target to fill a cell. Establish a suitable indicator, baseline and evidence plan, and follow the required planning process. Mark unresolved choices clearly.
Why are response rate and outcome rate separate?
They answer different questions. Coverage shows who is represented; an outcome rate describes a defined result within its stated denominator.
Does meeting a logframe target prove impact?
No. Target attainment is evidence of a defined result. Causal attribution needs an appropriate design and consideration of other explanations.
Can local programs use different collection forms?
Yes. Agree compatible definitions for the limited comparisons that matter, map local fields and keep additional local questions. Do not aggregate incompatible measures.
Choose your next step
King Center
Related practice: using program feedback for learning and review. The logframe, targets and results above are fictional, not findings from this customer story.
Read the customer story →