Free logic model template with 6 worked examples and an AI builder. Map inputs, activities, outputs, outcomes and impact — then connect every column to real data. Word, Google Doc and CSV.
Use Case · Program Design & Measurement
A program officer emails on Tuesday: the Q3 report is due Friday. You open your logic model template — a five-column table in Word, last edited when the grant was awarded. The outcomes are elegant. The measurement plan lives somewhere else: a spreadsheet of intake forms, a folder of survey exports, a shared drive of attendance logs. Nothing in the template connects to anything in the data. The next three days will be spent reconciling them.
This is the Model–Measurement Gap: the structural disconnect between what a logic model template says it will track and what your data collection system actually captures. It opens the moment the template becomes a PDF. It compounds every reporting cycle.
The fix is not a prettier template. It is a template that is the measurement architecture — every outcome column tied to a survey instrument, a collection cadence, and a persistent participant ID before enrollment opens.
This page gives you all of it: a free editable template, six worked examples, an AI builder that drafts your first version in under a minute, and the honest account of what a draft can’t do on its own.
A logic model is a one-page visual framework that maps how a program converts resources into change. It organizes program theory into five columns — Inputs, Activities, Outputs, Outcomes, and Impact — with causal arrows running left to right. It serves three jobs: program design clarity, funder communication, and the blueprint for what data to collect. A logic model becomes a measurement tool only when each column is attached to a matching data instrument — an intake question, a survey, or a follow-up tracker.
The five canonical components answer five different questions. The causal arrow runs left to right: inputs enable activities, activities produce outputs, outputs lead to outcomes, outcomes aggregate into impact.
Column 01
The resources the program requires — staff, funding, facilities, partnerships, curriculum.
Column 02
What the program actually does — training sessions, counseling, job placement, peer mentoring.
Column 03
The countable results — participants served, sessions delivered, graduates placed.
Column 04
Changes in behavior, knowledge, or condition — skills acquired, confidence gained, employment secured.
Column 05
The long-term change the program contributes to — economic stability, health equity, educational attainment.
Many funders use a four-component logic model — Inputs, Activities, Outputs, Outcomes — folding long-term impact into the outcomes column. The five-component version simply splits outcomes into short-term outcomes and long-term impact. Both describe the same causal chain; use the format your funder requests. A weak logic model is one where any link in that chain is unsupported by evidence the program can realistically collect.
Logic Model Examples
The difference between a template that gathers dust and one that produces evidence is the bottom row of every example below: the data field that makes each outcome measurable. Borrow the structure, swap in your program.
Trainers, curriculum, employer partners
Cohort training, job coaching, certification prep
Sessions delivered, participants certified
Interview confidence, technical skills gained
Employment at 90 days, earnings growth
Mentors, school partnership, training
Weekly 1:1 mentoring, group workshops
Mentoring hours, students matched
School engagement, sense of belonging
On-time grade promotion, graduation
Health educators, clinic space, materials
6-week workshops, coaching calls
Workshops held, participants enrolled
Self-management behaviors adopted
A1c reduction, fewer ER visits
Food supply, vehicles, volunteers
Weekly distributions, cooking demos
Households served, meals distributed
Reported food security, diet quality
Reduced household food insecurity
Instructors, learning lab, test vouchers
Classes, tutoring, practice testing
Instruction hours, practice tests taken
Reading/math level gains
HSE credential earned, enrollment in college
Case managers, employer network, housing partners
Case management, job placement, peer support
Plans completed, placements made
Stable housing, employment retained
Reduced recidivism at 12 months
Want one tailored to your program instead of borrowed? Generate it from a single sentence in the builder below.
Free Download
Every format below is the same five-column structure: Inputs · Activities · Outputs · Outcomes · Impact, with a sixth row for the matching data field. Fill it in directly, or generate a first draft with the AI builder and export to CSV.
Editable 5-column table with the data-field row built in.
Google DocOpen in Google Docs and File → Make a copy to edit.
CSVGenerate from one sentence, edit inline, export to CSV.
The fastest way to build a defensible model is to work backward from the outcomes you can realistically cause:
Best Practices · Logic Model Design
Every logic model template uses the same five columns. The difference between templates that produce evidence and templates that gather dust is the six rules below — applied at design time, not at reporting time.
Principle 01
If the template says the program will increase financial literacy, the intake survey must include a baseline literacy measure. Outcomes without instruments are reporting debts, not outcomes.
PDF templates enforce nothing — the gap opens silently.
Principle 02
The ID is assigned at enrollment, not reconstructed later from exports. Without it, pre-post, cohort, and longitudinal comparison all fail.
IDs added retroactively from emails lose 15–30% of records to typos and duplicates.
Principle 03
Gender, race, age, geography, income — every variable needed for equity reporting must be in the intake form. Retroactive capture means re-surveying.
Year-3 equity requests cannot be met from year-1 data that never asked.
Principle 04
Long-term outcomes need surveys at 90 days, 6 months, or 12 months. The template must specify cadence and instrument or those columns never populate.
Follow-up decided after the program ends catches 20–40% of the cohort.
Principle 05
Reduced community poverty is too large for one program. Stop at outcomes the program genuinely causes — not aspirational impact no instrument can detect.
Over-claimed outcomes fail evidence review; disciplined ones win renewal.
Principle 06
When a new funder uses different language, build a crosswalk — do not rewrite the outcomes. Cohort-to-cohort comparison breaks the moment the template changes its own words.
Three years, three labels — zero longitudinal comparison possible.
The common thread: every rule requires alignment between template and data system before the program starts. After enrollment, each rule costs ten times as much to enforce.
Template vs. Architecture
Word and PDF templates from major foundations satisfy grant submission. They do not satisfy measurement. Here is where the gap opens — row by row, every reporting cycle.
| Capability | Word / PDF template | Sopact Sense |
|---|---|---|
| Outcome–survey alignmentDoes each outcome column map to a data field? | Manual, after approval | Framework-first · generated from the columns |
| DisaggregationBreak outcomes down by demographic? | Only if intake captured it | Structured at intake · any variable becomes a filter |
| Persistent participant IDsOne person across every survey and follow-up | None · manual matching | Assigned at first contact |
| Pre-post comparisonBaseline vs. exit for matched participants | Spreadsheet match · drops 15–30% | Automatic via ID |
| Cohort-to-cohort comparisonOutcome-language consistency across cycles | Breaks when funders change | Structural · crosswalks, not rewrites |
| Qualitative integrationOpen-ended responses linked to quant outcomes | Separate file · manual theming | Linked and analyzed by participant |
| Reporting prep timeFrom data to funder-ready report | 11–14 days per cycle | Hours, not days |
| Follow-up deploymentLong-term tracking at 90 days / 6 months | Manual email · 20–40% response | Scheduled automatically |
Every row above is a cycle-by-cycle difference — not a one-time setup cost.
Most logic models fail not at the design stage but at the seam between design and collection. Five we see repeatedly:
A template also can’t validate whether your causal claims are defensible — that is the job of a theory of change, which is why rigorous evaluation needs both. And for the full measurement picture across a portfolio, see nonprofit impact measurement.
A logic model is a one-page visual framework that maps how a program converts resources into change — Inputs, Activities, Outputs, Outcomes, and Impact, with causal arrows left to right. It serves program design, funder communication, and the blueprint for what data to collect. It becomes a measurement tool only when each column is attached to a matching data instrument.
Inputs (resources the program requires), Activities (what the program does), Outputs (countable results), Outcomes (changes in knowledge, behavior, or condition), and Impact (the long-term change the program contributes to). Some templates compress these to four by merging short- and long-term outcomes.
Many funders use Inputs, Activities, Outputs, and Outcomes — folding long-term impact into the outcomes column. The five-component version splits outcomes into short-term outcomes and long-term impact. Both describe the same causal chain; choose the format your funder requests.
Yes. This page provides a free editable template as a Word document, a Google Doc copy, and a CSV — plus an AI builder that drafts from a one-sentence description and exports to CSV. The limitation of any Word or PDF template is that it is disconnected from your data system; it produces a design document, not a measurement architecture.
Work backward: name the impact, then the outcomes you can realistically detect, the outputs that signal them, the activities that produce them, and the inputs they require. Write one measurable change per outcome, keep claims within your span of control, and attach an indicator to every outcome before finalizing. The AI builder above produces a first draft in under a minute.
A sample logic model for a workforce program lists staff and curriculum as inputs, cohort training and job coaching as activities, sessions completed as outputs, improved interview confidence as a short-term outcome, and employment at 90 days as impact. This page includes six worked examples across workforce, youth, health, food security, education, and reentry — each with a matching data field for every column.
A logic model describes the program — what it does and produces. A theory of change argues for it — why the activities should produce the outcomes. Most funders request a logic model at application; rigorous evaluation needs a theory of change underneath. See theory of change vs logic model.
Yes — the builder on this page generates a structurally correct five-column framework from your program statement in under a minute and exports to CSV. What AI cannot do is build the intake form, assign persistent participant IDs, or enforce outcome-language consistency across cycles. Use AI to draft; use Sopact Sense to operationalize.
Every demographic variable needed for later disaggregation — gender, race, age, income, geography, program type — plus a baseline measure for every short-term outcome in the model. If the model promises to track improved confidence, intake must include a baseline confidence measure. Retroactive collection is impossible.
No. Sopact Sense accepts any existing template — Kellogg, Wisconsin-Extension, funder-specific, or custom — as the starting framework, then builds the matching intake forms, surveys, follow-up instruments, and disaggregation architecture around it so the columns connect to data.
Your next logic model
Sopact Sense is where the logic model template and the data architecture are the same document. Outcome columns drive survey design. Persistent stakeholder IDs link every response across intake, exit, and follow-up. Funder reports generate from the structure you set at design time — no reconciliation, no retroactive disaggregation scramble.
