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The complete guide to logic models — the five components (inputs, activities, outputs, outcomes, impact), the if-then chain, examples, and a builder.
A logic model is a one-page diagram that maps a program from the resources it commits to the results it expects, reading left to right across five components: inputs, activities, outputs, outcomes, and impact. It is the standard planning and reporting picture most funders ask for, and it answers one question well: what does this program do, and what should it produce?
A logic model describes; it does not explain. It shows that training leads to employment without saying why the link should hold. That is the difference between a logic model and a theory of change, and it is why Sopact treats the logic model as a live view generated from data rather than a slide drawn once and filed.
Used by: nonprofit program leads, grant writers, foundation officers, and evaluators who need a one-page results picture for a funder and a version that stays true as the program runs.
Every logic model threads the same five components in the same order. Inputs are what you commit: funding, staff, curriculum, partners. Activities are what you do with them: the sessions, services, or interventions you deliver. Outputs are the direct, countable products of those activities: sessions held, people served, credentials earned. Outcomes are the changes in the people served that follow: new skills, behavior change, a job. Impact is the long-term, systemic change the program contributes to.
The line most teams blur is outputs versus outcomes. Outputs are what you did; outcomes are what changed. “200 people trained” is an output; “68% placed in stable jobs” is an outcome. A logic model that stops at outputs counts effort; a logic model wired to outcome data measures result. Sopact keeps both on the same model, each outcome carrying an indicator and a data source rather than an empty box.
The classic logic model is a static artifact: drawn in a workshop, formatted for a grant application, and never touched again until the next report. That made sense when collecting and reading data was slow. It no longer does, and a logic model that has not changed since kickoff is a description of a program that no longer exists.
The shift is from a diagram to a live view. With Sopact, the five components are not empty boxes — each output and outcome is tied to an indicator, an instrument, and a persistent participant ID, so the model fills itself as data arrives and updates as the program runs. The logic model stops being a picture of the plan and becomes a picture of what is actually happening. For the causal layer beneath it — the mechanisms and assumptions a logic model leaves out — see the theory of change vs logic model guide.
Watch — the logic model, framework and build. What each of the five components holds and how to keep the model connected to real participant data instead of leaving it in a slide. Presented by Unmesh Sheth.
A logic model and a theory of change are not rivals; they scaffold different layers of the same program. The logic model is the operational layer — inputs, activities, outputs — legible to a funder in under a minute. The theory of change is the causal layer — the mechanism on every arrow and the assumption behind every outcome, the part that explains why the activities should produce the results. Build the theory of change first, then derive the logic model from it as the one-page summary; the full comparison and decision rules are in the theory of change vs logic model guide.
For funder contexts that ask for adjacent formats, a logframe adds indicators, means of verification, and assumptions in a matrix, and a results framework orders the results hierarchy. All three derive from the same underlying logic — build it once and switch the presentation, rather than maintaining three documents by hand. A blank canvas to start from is in the logic model template guide, and worked versions are in the examples guide.
A logic model earns its keep at four moments — drafting the five components, attaching an indicator to every output and outcome, collecting on one persistent ID, and keeping the model current each cycle. The animation below runs the loop; the four prompts under it are the ones behind each job.
1 · Build the five components. Draft inputs, activities, outputs, outcomes, and impact from what you already have. The walkthrough is in how to build a logic model.
Academy walkthrough → How to build a logic model
Build a logic model from this program description: [PROGRAM URL OR DOC]. Fill the five components — inputs, activities, outputs, outcomes, and impact — and for every output and outcome, name the indicator that measures it and the data source. Flag any outcome that is really an output in disguise.
2 · Audit outputs vs outcomes. Make sure every outcome is a real change, not a repackaged count, with a way to measure it.
Academy walkthrough → How to audit a logic model
Audit this logic model: [PASTE OR LINK]. Flag every outcome that is actually an output, every box with no indicator, and every activity that does not connect to a named outcome. Return a corrected five-component model with an indicator on each outcome.
3 · Add the causal layer. A logic model describes; derive it from a theory of change so the why is on record.
Academy walkthrough → How to build a theory of change
From this logic model: [PASTE OR LINK], derive the theory of change beneath it: name the mechanism on each arrow (why this activity produces this outcome) and the assumption each link depends on. Return the causal version the logic model summarizes.
4 · Convert to the funder's format. Turn the same model into the logframe or results framework a funder asks for.
Academy walkthrough → How to build a logframe
From this logic model: [PASTE OR LINK], produce a logframe: goal, purpose, outputs, and activities, each row with its indicator, means of verification, and assumption. Keep it consistent with the logic model rather than rebuilding from scratch.
The sections above are the argument; the Academy articles are the practice — each a hands-on companion written to run on your own data.
A logic model is a one-page diagram that maps a program from the resources it commits to the results it expects, across five components: inputs, activities, outputs, outcomes, and impact. It describes what a program does and what it should produce. In Sopact a logic model is kept as a live view generated from data — every output and outcome tied to an indicator and a participant ID — rather than a static slide.
The five components are inputs, activities, outputs, outcomes, and impact. Inputs are the resources you commit; activities are what you deliver; outputs are the direct countable products; outcomes are the changes in the people served; impact is the long-term systemic change. The most common error is listing an output ("200 people trained") in the outcomes column instead of a real change ("68% placed in stable jobs").
Outputs are what the program did — sessions held, people served, credentials issued. Outcomes are what changed in the people served — new skills, behavior change, a job. Outputs count effort; outcomes measure result. A logic model that stops at outputs cannot show whether the program worked; Sopact ties each outcome to an indicator and a data source so the outcome column carries evidence, not assertions.
A logic model describes what a program does in a left-to-right matrix; a theory of change adds the causal explanation — the mechanism on each arrow and the assumption behind each outcome. The logic model is the operational layer for funder communication; the theory of change is the causal layer for evaluation design. Build the theory of change first and derive the logic model from it as the one-page summary.
Yes — a logic model template is a pre-structured canvas with a labeled column for each of the five components. A template gets a team to a draft quickly, which is the point. The template is not the finished model, though: the team still supplies the indicators, instruments, and data sources that make each outcome measurable. A working template is in the logic model template guide.
Start from what the program already commits and delivers: list inputs and activities, then the outputs they produce, then the outcomes those outputs should lead to, then the long-term impact. For every output and outcome, name the indicator that measures it and the data source. Sopact drafts the five components from a program page and attaches an indicator to each, so the model starts measurable rather than decorative.
The static diagram drawn once and filed is over. A modern logic model is a live view: the five components are connected to indicators, instruments, and one persistent participant ID, so the model fills itself as data arrives and updates as the program runs. It stops being a picture of the plan and becomes a picture of what is actually happening — which is what Sopact is built to produce.